Sensor Networks

Hidden sensor network detects explosives

2010-01-31T03:55:00.000-08:00

A covert sensor system designed to identify and track people carrying explosives at busy transport hubs has been developed by researchers in Germany.

Dr Wolfgang Koch and his team at the Fraunhofer Institute for Communication, Information Processing and Ergonomics (FKIE) have built a prototype system named HAMLeT (Hazardous Material Localization and Person Tracking) to alert security staff to individuals intent on carrying out a terrorist event.

The system works using two separate sensory networks that gather chemical and kinetic information. The first is made up of a series of four to six rotating laser scanners that send pulses through corridors, walkways or escalators at airports or railway stations.

By calculating the time taken for the laser pulse to return the device, the scanner is able to measure the distance between the objects and construct a 2D image of the area.

This image is combined with information from a second network of electronic sensors hidden in air vents and wall fixtures that provide chemical data on explosive materials. Oscillating crystals on the sensor chips capture any threatening chemical molecules in the air and identify their composition based on the frequency of their oscillation.

Koch explained that used on their own, these sensors would be unable to provide the detail required to identify individual suspects. He said: ‘The basic idea of HAMLeT is to overcome the fundamental deficiency of sensors in assigning chemical data to a source by using sensor data fusion. The infrastructure at airports and railway stations always includes certain tunnel-like structures. If these tunnels are equipped with a number of sensors we can combine their output with kinematic data to identify high-risk individuals.’

Once all the information is gathered using these networks, it is analysed by algorithms based on Probabilistic Multi Hypothesis Tracking (PMHT). Any patterns identified by the algorithms are fed into advanced CCTV software, which automatically marks members of a crowd with a green, amber or red status indicating their threat level. Security personnel are then able to use their judgment on whether further action is needed.

Koch said: ‘What we have in mind is a system that is hidden and causes as little disturbance to public life as possible. We think this is probably a better alternative to the proposed full-body scanners as it will not cause journey delays or invade the privacy of passengers - one of the main concerns about some terahertz body scanners.’

In a trial involving the German armed forces, Koch’s team proved the system’s ability to track down five individuals carrying hidden explosives in a crowded entrance way. The team has also trialled the system in military harbour security and ferry traffic applications with promising results.

Koch said: ‘Of course, there are still some operational problems with the system. If a terrorist is aware of this type of observation they could try to prevent the smell or mask it with perfume. Another problem with the chemical sensors is that other things need to be taken into account, such as air ventilations that distort the results. We’re working on algorithms to solve this.’

The team is also working on a model that incorporates gamma-spectrometers for the identification of ‘dirty bombs’. These combine conventional explosives with radioactive material and could cause local contamination to an area. Further research will focus on the ergonomic design of tunnels and methods of reducing overcrowding to reduce false positives.

Koch said: ‘We don’t want to produce a George Orwell state with our technology. Although these systems are hidden, if correctly used they can maintain privacy while addressing the terrorist threat. Our results are encouraging and we hope to develop a robust system within the next few years.’

SIDEBAR: Detecting concealed objects

US firm Nesch has developed technology claimed to detect objects concealed inside the body better than conventional radiography.

Diffraction-Enhanced X-ray Imaging (DEXI) is non-invasive as it records high-resolution images of soft and dense tissue beneath skin but does not expose or recreate contours of a person’s body or face.

‘Carbon, nitrogen and oxygen do not absorb X-rays well. Explosives and narcotics are typically made of these elements. Conventional radiography detects these objects poorly due to its exclusive reliance on absorption,’ said Ivan Nesch, chief executive officer of Nesch.

‘DEXI…uses X-ray refraction and scattering to construct images, along with absorption. It can detect explosives and narcotics because they noticeably refract and scatter X-rays.’

Frost & Sullivan Evaluate World Temperature Sensor Market

2010-01-20T16:02:00.000-08:00

Proliferating applications ramp up prospects for the World temperature sensors and transmitters market, Finds Frost & Sullivan.

MOUNTAIN VIEW, CA /PRNewswire/ -- Although the world temperature sensors and transmitters market is considered to be mature, there are many competing temperature sensor technologies that are exhibiting high-growth prospects. Temperature is the most monitored parameter and plays a major role in various end-user applications, especially in critical and hazardous ones. Deployments span medical, heating, ventilation, and air conditioning, metals, food and beverages (F&B), automotives, and continuous-process applications. Temperature sensing has been growing steadily in importance, playing a vital role in industrial and R&D settings as well.

New analysis from Frost & Sullivan, World Temperature Sensors and Transmitters Market, finds that market earned revenues amounting to $3,635.2, million in 2009 and estimates this to reach $5,306.9 million in 2015.

"Every year, companies make large investments in focused research and development to improve existing temperature sensor functionality and develop new ones," says Frost & Sullivan Industry Analyst and Team Leader Dr. Rajender Thusu. "These efforts have the twin objectives of boosting demand for various temperature sensor systems and expanding penetration into newer application areas."

The vagaries in climate observed in the European region have necessitated the use of HVAC modules and consequently fuelled the demand for temperature sensors. Temperature sensors and transmitters are integral parts of the HVAC module and are increasingly fitted in climate control units as well as for monitoring refrigerant temperatures. This is true for all types of HVAC modules that are designed and manufactured for diverse applications in industrial uses, building automation, and automotives.

Advances in technology, coupled with a drop in price, have introduced new applications for HVAC modules. The market for HVAC modules is currently bigger, encompassing a variety of industrial and military applications. Flexibility in terms of electrical and mechanical parameters as well as a wide array of options in customizing the temperature sensors and transmitters is currently available.

Due to the growing number of new entrants, the market is highly competitive and fragmented. Pricing remains a key criterion while choosing a supplier.

"With the temperature sensors market reaching commodity status and prices expected to decline, suppliers are likely to be pressurized further," says Dr. Thusu. "This has resulted in the temperature sensors market being highly competitive."

The temperature sensor and transmitters market is characterized by the presence of large number of manufacturers focusing on most end-user and geographic segment. Considering that temperature measurement is a vital parameter in almost any process, the product has achieved deep market penetration. This is affecting growth opportunities for most sensor manufacturers. Additionally, volume discounting in the case of low-priced sensors is contributing to this restraint.

Price competition also remains a serious issue in the emerging economies, especially in countries such as China. Temperature sensor manufacturers recognize the growth potential of these emerging markets and are resorting to low pricing to penetrate these markets.

In order to sustain market demand, high precision, ease of integration, and competency are prerequisites. It has become imperative for companies to redefine their strategies to provide customers with instruments that are easily operable and have a very high level of reliability, keeping critical applications, high temperature environments, and portability in mind.

World Temperature Sensors and Transmitters Market is part of the Sensors and Instrumentation Growth Partnership Services program, which also includes research in the following markets: World Level Sensors and Transmitters Market, World Pressure Sensors and Transmitters Market, and World Flow Sensors and Transmitters Market. All research services included in subscriptions provide detailed market opportunities and industry trends that have been evaluated following extensive interviews with market participants.

For a virtual brochure of this study, please send an e-mail to Sarah Saatzer, Corporate Communications, at sarah.saatzer@frost.com, with your full name, company name, title, telephone number, company e-mail address, company website, city, state and country.

About Frost & Sullivan
Frost & Sullivan, the Growth Partnership Company, enables clients to accelerate growth and achieve best-in-class positions in growth, innovation and leadership. The company's Growth Partnership Service provides the CEO and the CEO's Growth Team with disciplined research and best-practice models to drive the generation, evaluation, and implementation of powerful growth strategies. Frost & Sullivan leverages over 45 years of experience in partnering with Global 1000 companies, emerging businesses and the investment community from 40 offices on six continents.

Linux-ready SBC controls robots, sensor networks

2009-12-23T15:55:00.000-08:00

The Linux-friendly Phidget line of USB-based sensing and control modules is used by hobbyists, robot-makers, and software developers, says the company. Phidgets, however, have always required tethering to a desktop computer via USB. Now Calgary, Alberta-ased Phidgets, Inc. is offering its first standalone SBC model, a full embedded computer that runs a Linux and which connects to a computer network via Ethernet or WiFi.
PhidgetSBC

The 1070 - PhidgetSBC is based around a Samsung SC32410 processor, which includes an ARM-920T core clocked at 266MHz, says Phidgets. The board further offers 64MB of RAM and 64MB of flash storage. The PhidgetSBC is equipped with a 10/100 Ethernet port, plus four full-speed USB ports that enable connection to other USB-only Phidgets.

As a result, the range of a Phidget network can now be extended well beyond USB’s 16-foot maximum. The USB ports also connect additional devices such as the supplied 802.11b/g USB wireless adapter, enabling remote control, or industry standard webcams, which are supported in firmware.

PhidgetSBC detail
(Click to enlarge)

The PhidgetSBC further integrates a PhidgetInterfaceKit 8/8/8, offering identical capabilities to an external model, which can also connect to the SBC to extend I/O further, says Phidgets. The built-in interface is said to offer connections to external sensors and other devices via eight analog inputs, eight digital inputs, and eight digital outputs. The PhidgetSBC package also includes a power supply, various cables, and a mounting kit (see image below).

PhidgetSBC and accessories
(Click to enlarge)

A variety of optional sensors are available, including distance, force, rotation, joy-stick, slide, reflective, humidity, voltage, pressure, vibration, temperature, touch, light, magnetic, motion, and current sensors. In January, 2008, for example, Phidgets introduced its 1123 and 1124 sensor modules for voltage and temperature sensing, respectively (pictured below, at right, with the 1123 on top).

The PhidgetSBC provides a custom Linux distribution built using Buildroot, and "exposes an easy to use interface for setting up and running custom applications on-board," says Phidgets. Applications written in either Java or C are supported with full shell access via a built-in SSH server. There are also GCC and development tools, the GDB debugger, and Linux command line tools, says the company.

The PhidgetSBC is said to be capable of operating autonomously, without the need for a GUI or remote connection. Applications include a variety of sensor networks, as well as robotics, where the combination of the SBC, WiFi, connected sensor modules, and webcams enable a remote controlled robot, says the company.

Phidget modules purchased for use with the PhidgetSBC can also be controlled via a PC, using an application programming interface (API) that allows applications to be developed in .NET, Visual Basic, VBA, LabView, Java, Delphi, C and C++. Desktop operating systems supported include Linux, Windows XP, Windows Vista, Windows CE, and OSX, according to the company.

Int'l conference on wireless communications

2009-12-22T16:19:00.000-08:00

ALLAHABAD: The fifth international conference on wireless communication and sensor networks (WCSN-2009) jointly organised by the Indian Institute

of Information Technology, Allahabad and Institute Electrical and Electronics Engineers Conference would begin on Jhalwa campus here from Thursday. Prof Dharma P Agarwal from University of Cincinnati, US would inaugurate the conference.

A total of 40 papers from different countries including South Korea, United Kingdom, Malaysia, etc would be presented by speakers. Nearly 120 papers were received out of which 40 have been finally selected for this international conference, said Dr Rajat Singh, organising committee member.

IIIT-A director and conference chairman, Dr MD Tiwari said: "Wireless communication and sensor networks have an important place in creating the ubiquitous environments that would have profound influence on the society." Prof M Radhakrishna, co-chairman said that speakers would primarily focus on wireless sensor networks, architectures and protocols, wireless sensor network architectures, flexible, semantic wireless sensor networks, energy-aware network protocols and power management, localisation and synchronisation, security and privacy in wireless sensor actuator networks.

Prof US Tiwari, co-chairman said that the conference would also include the topics such as sensors and technologies for WSAN, architecture, design and fabrication of smart wireless sensors, motes and RF tags, sensor technologies and smart dust, MEMS and Nanotechnologies, biosensors, real time operating systems for WSANs and programming environments.

Dr Shekhar Verma, Coordinator said that under WSAN application, deployment and experiences, various themes such as surveillance, disaster prediction and management, transportation and traffic management, security and defence, environmental monitoring and ecology, mining, industrial production, building construction and monitoring, entertainment and interfacing with real world applications will be discussed by the experts.

Wireless sensor and actor networks (WSAN) would influence the world through their pervasive nature in remote distributive monitoring and control by taking advantage of developments in wireless communication, embedded systems, semantic web, and smart surroundings, etc. Despite its promise, many breakthroughs in power aware designs, availability of wireless channel bandwidths, etc, are needed.

To build large and sustainable systems, these energy and bandwidth constraints present fundamental challenges in developing intelligent, distributive, collaborative, multi-modal networked objects that sense and act in wide areas and in unattended environments. The conference tries to spur these developments by bringing the researchers together to share their research, experiences and their perceptions, added Dr Tiwari.

ExScal: Extreme Scale Wireless Sensor Networking

2009-12-08T05:48:00.000-08:00

Overview

In December 2004, the OSU DARPA-NEST team headed by Anish Arora completed the first demonstration and experiments of ExScal. This demonstration covered an area 1.3km by 300m with about 1000 sensor nodes and around 200 backbone nodes making it the largest wireless sensor network assembled to date. ExScal's demonstration is also the largest ad hoc 802.11 network thus far created.

It is widely believed that someday there will be sensor network deployments of hundreds of thousands of nodes. The challenges in scaling to networks of this size are quite different than the ones encountered in fielding much smaller networks of dozens or hundreds of nodes. The former subsumes the latter and add a host of new problems. The motivation for the DARPA Extreme Scaling project, code-named "ExScal," was to investigate the challenges in scaling to a network of 10,000 sensor nodes. While 10,000 nodes is still a fraction of "hundreds of thousands," we have encountered many of the basic challenges of extreme scaling that we believe will be encountered with larger numbers of nodes. Consequently, the ExScal project has provided us with a rich set of experiences and has given us a visceral understanding of the "real" problems that are posed by networks of extreme scale.

Figure 1.1: Inside an XSM

Figure 1.2: ExScal Demonstration Topology

Equipment

The Sensor-Actuator Nodes

The sensing and actuating nodes used in the project, called XSMs (Extreme Scale Mote), were designed specifically for this project by The Ohio State University and CrossBow Technology, and manufactured by CrossBow Technology. They feature a variety of sensors and actuators including a magnetometer, a microphone, four passive infrared receivers, a photocell, a sounder, and feedback LEDs. The XSMs were installed with a trusted software base, also known as the "factory image". This default software includes the Deluge Network Reprogramming Service and the Sensor Network Management System both of which are provided by the University of California at Berkeley (UCB). They also have simple power management capability. The ExScal application consists of the software components deployed during the on-field operation of the XSMs. The ExScal software provides the motes with reliable communication, routing, localization, fault tolerance, and applications to utilize the onboard sensors. The XSMs comprise Tier 1 of the network; more information on their design can be found here.

Figure 2.1: A deployed XSM in its usual casing

Figure 2.2: A Stargate circuit board

The Backbone Network Node

The XSMs were organized under a second tier of devices called Extreme Scale Stargates (XSS) running the Intel Stargate platform. ExScal customized the stargates by adding an 802.11b Wireless Networking card with requisite software, an external antenna, a housing for the device, and a battery pack. The stargates were placed strategically in the topology (seen above) such that most motes were able to communicate with a stargate. These, Tier 2, nodes ran a controller application that served to orchestrate the localization and reprogramming services at Tier 1. They also facilitated retrieving data from the motes to be analyzed on PC's (Tier 3).

Application

There are many applications for an large scale sensor network. The application ExScal is developing for is the detection & classification of multiple intruder types over an extended perimeter. This would be ideal for protecting an area that is too vast to be patrolled by human guards such as an oil pipeline or a national border. To this end, the data retrieved from Tier 1 is used to visualize and track intruders in the sensor network. The visualization system is able to display the motes that detected a target, the target using an icon, the path any target has taken through the network, the topology of the sensor network, and the overall health of the network. This is done using group bassed classification. Teir 3 can identify how many XSMs detect a given object and use this "influence feild" to determine what type of object is being sensed. Below is an example of how different types of intruders appear to a single XSM. The images are frequency spectrums of data collected from the passive infrared receivers. The car's energy is in a higher frequency band than the human's energy.

Figure 3.1: A Human 10m Away

Figure 3.2: A Car 25m Away.

Wireless Sensor Network Solutions Launched for Research Labs & Universities in India

2009-11-04T15:44:00.001-08:00

Bangalore, 22 September’09: Dreamajax Technologies, a Bangalore based technology firm with expertise in the areas of Web Application Development, Ad Server implementation & Customization and Design & Development of Wireless Sensor Networks has launched innovative solutions in the field of Wireless Sensor Networks (WSN) for the Indian Market.

Specifically targeted at Research Organizations & Educational Institutions, WSN India, the Research & Development wing of Dreamajax Technologies will help setup labs and provide solutions to get started with research in the field of Wireless Sensor Networks. Such facilities were only available to large organizations who could afford higher investments, but today even a small organization or educational institute can afford this setup.

“In the comfort and safety of a lab, a researcher or student can monitor the activity of an active volcano or a cyclone which is miles away. Wireless Sensor Networks are making these and other applications a reality. From surveillance systems to building automation and control to habitat monitoring, Wireless Sensor Networks have the potential to change the world as we know it” says Ashwin K Whitchurch, CTO of Dreamajax Technologies.

WSNs can be used in Agriculture & Environmental Studies, Medical & Health Care Studies, Aeronautical Labs, Industrial Labs, Civil Engineering, Electronics and Communication, Mechanical Engineering, Marine & Wildlife Conservation to name a few.

WSN India will provide complete assistance in the setup and implementation of these labs with WSN Nodes to collect data, User Interface Software for real-time monitoring along with complete training modules and materials.

Ram Kumar, Founder & CEO of Dreamajax Technologies says, “Our main aim is to simplify access to technology & tools and reduce setup costs using ready made platforms developed by us thereby making it affordable to the educational and research fraternity."

About WSN
Wireless Sensor Networks (wsnindia.com) are basically tools for efficiently acquiring data from several sensors placed at strategic locations. They are networks of geographically distributed sets of sensors on an autonomous platform (called WSN nodes) which co-operatively enable monitoring of a physical parameter or environmental condition such as temperature, humidity, pressure, light, sound, motion, acceleration or anything that can be sensed.

About Dreamajax Technologies
With over 400 clients across the globe including several big names in the field of online advertising, Dreamajax strives to be the market leader in the field of Digital Media (Ad Delivery Engines) by providing effective and innovative formats to satisfy industry demands.
Dreamajax is the proud Winner of the Karnataka State IT Export Award 2008 during BangaloreIT.biz held in November 2008.

GE Developing Body Sensor Networks

2009-11-04T15:42:00.000-08:00

Waukesha, Wis.-based GE Healthcare is unveiling an initiative aimed at developing wireless medical monitoring systems, or body sensor networks (BSN.)

GE Healthcare, in conjunction with GE’s technology development arm, Global Research, is now developing BSNs, which consists of sensor devices that collect critical patient-specific information, including temperature, pulse-oximetry, blood glucose levels, electrocardiogram readings, blood pressure levels and respiratory function. This real-time patient information can be collected and transmitted to doctors and nurses to enable efficient patient monitoring from any location, says the company.

Additionally, BSNs would eliminate the need to disconnect and reconnect wires as patients change care areas. This is critical, says GE, as patients often require varying levels of care throughout their hospital stay, progressing from low-acuity to high-acuity and back to low-acuity before discharge. With BSNs, caregivers will be able to quickly add or remove parameter sensors as medical conditions warrant, integrating and evaluating parameters to make informed treatment decisions, it touts. Additionally, BSNs will allow caregivers to wirelessly monitor parameters outside of specialized care areas.

Team Building Wireless 'Electronic Noses' Using Nanoscale Sensors

2009-10-24T19:29:00.000-07:00

ECE researchers are developing multifunctional gas nanosensors integrated with wireless communications, signal processing and readout capabilities. (Diagram) Conceptual diagram of a wireless gas sensor node with nanosensor devices that are assembled on an IC chip. (Photo) A scanning electron micrograph of a proof-of-concept prototype. In this case, a Rhodium nanowire — about 3 micrometers long x 300 nanometers in diameter (see inset) — was assembled on a prototype Silicon CMOS IC.

ECE researchers in the Wireless Microsystems Laboratory are working to integrate functional nano-scale gas sensors with wireless communications microsystems — using a new assembly process that would allow low-cost batch fabrication and the ability to incorporate distinct sensor devices to monitor different chemicals and gases.

Their ultra-miniature "electronic nose" would be sensitive to a variety of gas and chemical compositions and incorporate readout, signal processing, and communications circuitry.

Ultra-Sensitive, Low-Power Sensors
"Sensory devices with nanometer-scale dimensions can give us ultra-high sensitivity," said Sanjay Raman, director of the laboratory. Sensitive nano-sensors can detect their target at the molecular level, he explained. "For example, in gas detection, this can give a very early warning, allowing people to react before any damage is done," he said.

Networks of Nano-Sensors
"We are working to integrate nanosensors with smart silicon circuitry, so the sensor microsystem can sense, think, and communicate," he said. Independent sensing nodes can then be deployed in networks for situations ranging from exterior or interior environments and structures, to miniature, remotely piloted vehicles. The sensing networks can be used for real-time monitoring of vehicles and structures, such as roads or bridges, environmental monitoring for health and safety, and security and battlefield surveillance.

Assembled Devices
Raman's team is collaborating with Stephane Evoy of the University of Pennsylvania to develop the gas nanosensor systems. Instead of using the conventional method of direct machining the nano-devices from the substrate, they are using a "bottom-up" assembly process.

Their goal is to design and fabricate silicon IC chips with integrated wireless communications, readout, and sensor assembly functions, and subsequently assemble the nanosensor devices onto the prefabricated chip. The nanosensors are assembled by placing a drop of fluid containing suspended nanowires and nanorods on the chip. Using probes, electrical stimuli are applied that polarize the suspended nanowires and coax them to assemble on and between selected electrodes.

Batch Fabrication
The batch fabrication assembly technique is expected to reduce manufacturing costs compared to other nanomachining processes. "This technique is compatible with standard foundry technologies," Raman said, which is important for low-cost manufacturing of the devices.

The gas sensor project is funded by a contract from the National Science Foundation (NSF). For more information, please visit the Wireless Microsystems Laboratory website at www.ece.vt.edu/wml/.

Research Team Maximizing Wireless Video Sensor Network Life

2009-10-24T19:28:00.000-07:00

Base station placement and network topology may be key factors affecting the lifetime of wireless video sensor networks, according to Thomas Hou and Scott Midkiff, who recently received a $225,000 NSF Information Technology Research (ITR) grant to study and improve video sensor network lifetime. The ITR program funds about 10 percent of all submitted proposals.

"A major challenge with a wireless video sensor network is maximizing the lifetime of the network given that there is limited battery power at each node and that replacing or recharging the batteries is usually not feasible," Hou said. "An analysis of power dissipation at the nodes suggests that wireless communication consumes significantly more energy than any other node activity," he added. "Thus, if we can optimize the communication power consumption behavior of the sensor node, we can extend the network lifetime."

The team is exploring several definitions for network lifetime, taking into account the percentage of nodes required to remain alive and the different priorities of nodes based on their locations. The researchers are studying base-station placement and multihop routing, base-station placement constraints, and dynamic varying network topology.

"We have found that the techniques and algorithms from the field of computational geometry can help us understand the problem, study the impact of topology control, and develop performance bounds needed for this research," Hou said.

After determining topology control techniques, the team will develop a software toolkit that can be used by sensor network designers to optimally perform network topology control.

For more information on research regarding wireless video sensor networks, please visit the website at www.ece.vt.edu/~thou/.

Image Sensors Everywhere

2009-10-13T17:29:00.001-07:00

Earlier this week, the Royal Swedish Academy of Science awarded the Nobel Prize in Physics for 2009 to the creators of, respectively, fiber-optic communications and digital imaging. Although fiber-optics has proven immensely valuable for both global communications networks and for fiber-optic sensing, today I'm going to concentrate on the digital imaging portion and reflect, briefly, on what a game changer this technology has been.

I think it's safe to say that, without digital imaging, we wouldn't have achieved the strides in astronomy, medical imaging, manufacturing automation, and (more recently) automotive safety that we have. And that doesn't even touch on how digital photography has affected so many people.

Being able to place image sensors on spacecraft and in land-based observatories means rapid retrieval and analysis of those images and the data they contain. It also means that image sensors sensitive to other regions of the electromagnetic spectrum can be used and that, as the image sensors become faster or more sensitive or larger, that the data gathered are both more plentiful and more accurate.

Digital X-rays means that the radiology tech taking the pictures can see immediately whether the images they've taken are any good; because the imagers themselves are so sensitive to X-rays, the patients are exposed to less radiation; and, last but by no means least, the images are easily transferred and digitally enhanced. We've got cameras we swallow and cameras that enable laporascopic surgery and other explorations of the human body.

Within the industrial automation field, machine vision has revolutionized some forms of manufacturing automation, moving QC throughout the production process and enabling earlier identification and correction of problems. The increasingly sophisticated image analysis software coupled with better and faster image sensors means ever greater production speeds and further process automation.

In the military applications we've got image sensors in UAVs such as the Predator, better surveillance cameras, cameras on bomb-disposal robots, night vision goggles, and thermal imaging (with its helpful civilian uses) to name just a few.

Image sensors in cars are a more recent development, but they're being used to help people to park, to keep a watchful eye on blind spots, to warn of lane departures, and to dim headlights automatically. I am very sure that additional applications are under development as we speak.

And, finally, digital photography allows us, professional or amateur, to take more pictures, possibly better ones, and to share them easily. Tell me which applications I've missed!

So, a very hearty thank you and congratulations to Willard S. Boyle and George E. Smith.

Micro force sensor

2009-10-07T17:23:00.000-07:00

Smaller than a no. 2 pencil tip, this submergible micro-force transducer is ideal for medical research applications. The AIFP® is constructed from micro-machined stainless steel and heat treated to form an elliptical spring microstructure, which deforms elastically when loaded. This construction allows the device to accurately and reproduceably measure applied forces.

A quarter bridge strain gauge bonded to the structure provides a sensititve output, linearly proportional to force. The strain gauge's self temperature coefficient is matched to the stainless steel substrate to minimize the effect of temperature on probe output. The elliptical cross section and 3-conductor flat tape cable keep the device anchored and oriented within fibrous materials. In addition, bridge completion resistors are included in the AIFP's® integral connector.

For implantation, a trocar and slotted cannula are used to bury the transducer within the tissue substance. A suture may be placed in an aperture located in the wall of the probe to facilitate probe removal. The AIFP® is coated with high temperature polyimide and parylene coatings for protection against moisture and saline solutions.

Sensor Networks and Sustainability: “Connecting Real, Virtual, Mobile and Augmented Spaces”

2009-09-17T17:19:00.000-07:00

Today, I did a presentation, on connecting real, virtual, mobile, and augmented spaces to support sustainability, for Earth Week SL, with Dave Pentecost and Jim Purbrick, who presented on Carbon Goggles.

Dave and I focused on sensor networks, open data, Pachube, OpenSim, and sustainability from perspective of, “hack local, think global.” Dave and I will be picking up on some of these themes of sensor networks and sustainability next week in our presentation with Dimitri Darras at ITP, NYU, Aprl 24th, 6.30 pm to 8 pm – details here. If you are in New York City, I hope to see you there.

We got some interesting insights into augmented reality from Jim Purbrick whose Carbon Goggles project prototypes how we can use augmented reality to read carbon identity and to combine well organized, verified data from AMEE – a neutral aggregation platform to measure the “carbon footprint” of everything on earth, with crowd sourced tagging and linking.

Shaspa – “the sensor network system that has it all”

We also discussed, recently launched, Shaspa. Shaspa’s energy management packages connect spaces – real, virtual, mobile and augmented. Shaspa has been blogged by Maxping and Virtual World News, so you can read all about it, but the Shaspa device kit won’t be available until next week. Some key features of the Home Energy package are listed on the slide above. However, this evening, Dave Pentecost and I got a sneak preview of both the Shaspa commmunity and enterprise hardware and software packages from Shaspa founder Oliver Goh. We were pretty impressed.

Dave: “It’s the ultimate hackable device for energy management!”

Oliver: “Bring us any sensor device – with documentation, and within three days we will put a driver into Shaspa.”

Oliver is on the right and Dave on the left in the picture above. The picture below shows Shaspa in OpenSim. Oliver and I will be attending the 3D Training, Learning and Collaboration Conference in Washington, DC, next week.

Two New Contiki Ports: MicaZ and Sensinode

2009-09-16T17:18:00.000-07:00

As of yesterday, we have two new Contiki targets in the development code: one port for the Crossbow MicaZ, a popular prototyping and research platform in wireless sensor networks, and one port for the Sensinode CC2430-based system-on-a-chip N100/N600/N601/N710/N711 platforms. The MicaZ port was developed by Kasun Hewage from the University of Colombo School of Computing, Sri Lanka, and the Sensinode port by Zach Shelby, head of research at Sensinode.

An Overview of Cryptography

2009-09-14T17:23:00.000-07:00

1. INTRODUCTION

Does increased security provide comfort to paranoid people? Or does security provide some very basic protections that we are naive to believe that we don't need? During this time when the Internet provides essential communication between tens of millions of people and is being increasingly used as a tool for commerce, security becomes a tremendously important issue to deal with.

There are many aspects to security and many applications, ranging from secure commerce and payments to private communications and protecting passwords. One essential aspect for secure communications is that of cryptography, which is the focus of this chapter. But it is important to note that while cryptography is necessary for secure communications, it is not by itself sufficient. The reader is advised, then, that the topics covered in this chapter only describe the first of many steps necessary for better security in any number of situations.

This paper has two major purposes. The first is to define some of the terms and concepts behind basic cryptographic methods, and to offer a way to compare the myriad cryptographic schemes in use today. The second is to provide some real examples of cryptography in use today.

I would like to say at the outset that this paper is very focused on terms, concepts, and schemes in current use and is not a treatise of the whole field. No mention is made here about pre-computerized crypto schemes, the difference between a substitution and transposition cipher, cryptanalysis, or other history. Interested readers should check out some of the books in the bibliography below for this detailed — and interesting! — background information.

2. THE PURPOSE OF CRYPTOGRAPHY

Cryptography is the science of writing in secret code and is an ancient art; the first documented use of cryptography in writing dates back to circa 1900 B.C. when an Egyptian scribe used non-standard hieroglyphs in an inscription. Some experts argue that cryptography appeared spontaneously sometime after writing was invented, with applications ranging from diplomatic missives to war-time battle plans. It is no surprise, then, that new forms of cryptography came soon after the widespread development of computer communications. In data and telecommunications, cryptography is necessary when communicating over any untrusted medium, which includes just about any network, particularly the Internet.

Within the context of any application-to-application communication, there are some specific security requirements, including:

Authentication: The process of proving one's identity. (The primary forms of host-to-host authentication on the Internet today are name-based or address-based, both of which are notoriously weak.)

Privacy/confidentiality: Ensuring that no one can read the message except the intended receiver.

Integrity: Assuring the receiver that the received message has not been altered in any way from the original.

Non-repudiation: A mechanism to prove that the sender really sent this message.

Cryptography, then, not only protects data from theft or alteration, but can also be used for user authentication. There are, in general, three types of cryptographic schemes typically used to accomplish these goals: secret key (or symmetric) cryptography, public-key (or asymmetric) cryptography, and hash functions, each of which is described below. In all cases, the initial unencrypted data is referred to as plaintext. It is encrypted into ciphertext, which will in turn (usually) be decrypted into usable plaintext.

In many of the descriptions below, two communicating parties will be referred to as Alice and Bob; this is the common nomenclature in the crypto field and literature to make it easier to identify the communicating parties. If there is a third or fourth party to the communication, they will be referred to as Carol and Dave. Mallory is a malicious party, Eve is an eavesdropper, and Trent is a trusted third party.

Configure secure wireless networking on SonicWALL routers

2009-09-13T17:18:00.000-07:00

Wireless networking is a business necessity within most organizations. From traveling employees to guest visitors, wireless networks often prove the best method for connecting these users to e-mail, the Internet and local file shares.

Numerous challenges arise when wireless networks are deployed. Besides ensuring a wireless access point produces sufficient radio strength throughout a work area, such network communications also must be secure. In addition to making it difficult for unauthorized users to access the wireless network, organizations must take steps to encrypt data that passes between wireless devices and the wireless access point itself.

Several SonicWALL routers boast wireless connectivity. As with most any wireless-enabled router today, SonicWALL models also feature security technologies aimed at protecting wireless transmissions.

Wireless-specific versions of SonicWALL’s TZ 150, TZ 170, TZ 180 and TZ 190 models support wireless networks. All of SonicWALL’s latest PRO series devices (including the 1260, 2040, 3060, 4060, 4100 and 5060 models) support optional 802.11 wireless networking.

Combined with an appropriate TZ or PRO model, SonicWALL SonicPoint devices enable extending secure wireless technology throughout an entire facility. A TZ 170 appliance, for example, automatically detects SonicPoints deployed on the network. Security settings can then be configured from the TZ 170’s interface, which enables centralized administration. Here’s what’s involved configuring wireless network settings and security using a SonicWALL TZ 170 appliance.

SonicWALL Wireless Configuration Wizard

To access SonicWALL’s Wireless Configuration Wizard, log on to the SonicWALL router, then click the Wireless button found within the left navigation bar of SonicWALL’s Web-based management interface. The Wireless Status page will be displayed. Click the Wireless Wizard button that appears toward the top-right of the screen.

The SonicWALL Wireless Configuration Wizard will appear. Click the Next button to proceed with the wireless administration utility.

The wizard’s first step will appear as seen in Figure A. Step one addresses wireless local area network (WLAN) network settings. You must specify the WLAN IP address and subnet mask before continuing. In addition, a checkbox is provided for enabling Windows networking support between the wired local area network and the WLAN. Be sure to check the box to enable communication between the LAN and WLAN. Then, click Next.

Figure A

Step one of the Wireless Configuration Wizard requires specifying a WLAN IP address and subnet mask.

Step two requires setting the Service Set ID (SSID), radio mode, country code and channel. The SonicWALL wizard provides a blank field for specifying the SSID. The default SSID is sonicwall. Consider changing the SSID to a value that reveals nothing about the organization or office in which it is deployed. I typically deploy SSIDs as HomeNetwork and BusinessNetwork, as doing so provides potential hackers with less information about a network (particularly in more heavily populated areas such as apartments and office parks).

On most SonicWALL routers, including the popular TZ 170 wireless model, three radio modes are available: 2.4GHz 802.11 b, 2.4GHz 802.11g or 2.4GHz 802.11g and b Mixed mode. Using the provided drop-down menu, select the mode you wish to use within your organization.

Figure B

It’s a good idea to change the default SSID when deploying any wireless network.

Units shipped within North America typically feature two country codes: United States (US) and Canada. Using the Country Code drop-down menu, specify the appropriate code.

The last item to configure within Step 2: WLAN 802.11b/g Settings is the channel. Channels one through 11 are available options. Or, AutoChannel is an alternative choice and, in fact, the default. Choosing AutoChannel allows the wireless network equipment to automatically negotiate the best channel settings. Once the channel is specified, clicking Next continues to the next step.
Step 3: WLAN Security Settings enables configuring one of three security modes for the

SonicWALL wireless router. The three security mode choices are:

WiFiSec VPN Security – The default selection, WiFiSec VPN Security creates an IPSec-powered VPN over which the wireless traffic travels.
WEP + Stealth Mode – Utilizes Wired Equivalent Protection (WEP) to secure wireless communications.
Connectivity – Implements wireless communications featuring no encryption or access controls.

When selecting WiFiSec VPN Security and clicking Next, the Step 4: WiFiSec – VPN Client User Authentication page appears. This step helps create a user name and password for a new user with VPN client access privileges. The WLAN WiFiSec security setting will enable the SoniCWALL Group VPN feature. If you wish to edit user privileges, you can log on to the SonicWALL router’s Web-based administrative interface and edit user permissions by clicking Users | Settings.

After a user name and password are supplied, click Next to proceed to the wizard’s fifth step. The Step 5: Wireless Guest Services screen allows you to enable guest wireless access while also specifying the guest account name, password and account and session lifetimes.

Once these values are supplied, click Next to view a Wireless Configuration Summary. Clicking Apply prompts the SonicWALL Configuration Wizard to apply the settings and configuration parameters you’ve entered into the wizard. When the wizard completes, a confirmation page should then appear (with a Finish button being the only available option).

Figure C

The Wireless Configuration Summary confirms settings, and enables administrators to review configuration details, before the wizard makes changes.

If, when completing the Wireless Configuration Wizard, you select WEP + Stealth Mode in Step 3, the wizard’s fourth step will prompt you to provide WEP information. The Step 4: WEP + Stealth Mode Settings screen enables configuring 64- or 128-bit WEP encryption. Select the value you require from the WEP Key Mode drop-down box. Once the WEP mode is specified, you must enter the actual WEP key, which you can do using alphanumeric or hexadecimal text. Select the appropriate radio button and click Next. Completing the wizard, using WEP and Stealth Mode Settings, then completes the same as when selecting WiFiSec VPN Security.

Wireless Status Information

With the wizard complete, the SonicWALL’s wireless network configuration goes live. You may confirm proper configuration by logging on to the SonicWALL appliance and clicking Wireless. The Wireless | Status page appears. In addition to displaying WLAN Settings, the menu also displays WLAN Statistics and Station Status information as shown in Figure D.

Figure D

The SonicWALL Wireless Status screen displays critical WLAN information.

Within WLAN Settings, the SonicWALL Web-based administration interface tracks whether the WLAN is enabled, as well as the active SSID, WLAN IP address, WLAN subnet mask, channel in use, radio transmission rate, authentication type, MAC filter status and more.
The WLAN Statistics menu tracks receive and transmission statistics for unicast and multicast frames, total packets, total bytes, discards, aborted frames and more.

Station Status, meanwhile, lists the names, signal strength and authentication status, among other items, for all wirelessly connected systems.

Editing wireless settings

In the event preconfigured wireless network settings must be changed, repeating the Wireless Configuration Wizard is unnecessary. Instead, you can manually edit required settings. To manually update WLAN settings, log on to the SonicWALL router and click Wireless from the SonicWALL Web-based management console. Six items appear within the Wireless sub-navigation menu: Status, Settings, WEP/WPA Encryption, Advanced, MAC Filter List and IDS. Figure E shows what it looks like.

Figure E

SonicWALL’s Wireless Settings sub menu enables tweaking the WLAN IP address, subnet mask, SSID and more.

To disable the wireless LAN, change security and encryption parameters or update the radio role (Access Point and Wireless Bridge are the two options), change SSID or re-configure WLAN IP address, radio mode, country code or channel, click Settings. Make the required changes within the Settings menu, then click the Apply button to save and enable the changes.

To actually change encryption authentication type (such as migrating from WEP to WPA), click the WEP/WPA Encryption button found on the SonicWALL’s left navigation bar. Using the WEP/WPA Encryption menu, you can configure encryption level and security keys.

From the Advanced menu, reached by clicking the Advanced button from within the Wireless sub-navigation menu, you can adjust beacon interval (and hid the SSID within the beacon). You can also specify the maximum number of permitted client associations (the default is 32 on the TZ 170). Other Advanced settings include antenna diversity and transmission power. Once changes are made, remember the Apply button must be pressed to save and implement any updates that are made. Should you wish to return Advanced settings to factory presets, click the Restore Default Settings button, which is found at the very bottom of the page.

Organizations wishing to allow or deny wireless connections to specific systems can implement MAC filtering. When MAC addresses are entered into the SonicWALL’s MAC Filter List, you can choose to Allow or Block communications for each device that is entered. You can also enter comments, such as Guest system or Unknown workstation from neighboring company, within a Comment field to help other IT professionals better understand why certain systems have been allowed or denied specific wireless access.

To filter devices based on their media access control addresses, click MAC Filter List and then click the Add button. Supply the MAC address of the system, supply any relevant comment, and then (using the Action drop-down menu) specify Allow or Block. When you’re done, click OK to add the entry to the MAC Filter List.

SonicWALL’s wireless-equipped routers also include intrusion detection capabilities. Click IDS from the Wireless sub menu to access the feature. Besides logging nearby access points (within the Discovered Access Points section), the IDS page allows you to enable probing, flood detection and rogue access points. Using the Authorized Access Points menu, you can add specific entries for access points for which you wish to allow WLAN operation. Just click the Add button, enter the station’s BSSID and any relevant comment, then click OK.

Summary

SonicWALL routers provide excellent security by enabling secure communications with remote employees and wireless users. The device’s wireless configuration wizard simplifies the task of configuring secure wireless communications. Sometimes settings must be tweaked or customized, however. The SonicWALL’s Web-based management interface simplifies the process of not only monitoring active WLAN connections, but customizing wireless communications as required.

CTIA Wireless 2008 goes almost green

2009-09-13T17:12:00.000-07:00

* Author: Bill Detwiler

I’ve been attending tech conferences from the past 10 years and have always been dumbfounded by the mountains of paper these events waste. I would wager most of the show guides, press releases, product brochures, and expo floor maps end up in a landfill instead of being recycled. Yet as the “green movement” gains support in the corporate world, many businesses and organizations are at least trying to reduce their waste output.

CTIA Wireless 2008 - Goes Green

At CTIA Wireless 2008, conference attendees were asked to join the green effort by placing old show badges, unwanted show materials, and even old cell phones in collection bins placed around the Las Vegas Convention Center. Beyond getting attendees involved, CTIA touted their own green initiatives, such as printing materials on recycled paper, switching to a single-piece badge instead of a three-piece design, encouraging exhibitors to deliver material electronically, and even asking the convention facility to use biodegradable materials in the concession areas.

CTIA Wireless 2008 - Recycling Centers

I’m glad CTIA is making an effort to reduce wastes and recycle, but I think they’re missing at least one opportunity to lessen their environmental impact–stop printing the massive CTIA Show Program and Directory. Even if the show guide is printed on recycled paper, I would imagine that most will end up in the trash bin and not a recycling center.

CTIA Wireless 2008 - Show Program and Directory

I didn’t have a chance to weigh the CTIA Wireless 2008 tome, but I didn’t carry it back on my flight because I was worried it would put my checked bag over the airline’s weight limit. And, there was no way I was carrying it back on my shoulder. Does the guide really need to be this large? I now the book is an advertising opportunity for exhibitors and show sponsors, but aren’t there more effective ways to interact with attendees?

If CTIA wants my advice for improving their green initiative, here are my suggestions for next year’s Show Program and Directory:

Airbus urges speed sensor switch

2009-09-09T17:01:00.000-07:00

Plane manufacturer Airbus has urged airlines to change the make of the majority of speed sensors on about 200 long haul aircraft.

Airbus has issued a bulletin to airlines recommending that they switch the parts, also known as pitots, to those made by US manufacturer Goodrich.

The moves comes as investigations continue into the cause of the fatal crash of an Air France Airbus in June.

Investigators have said speed sensors, or pitots, may have been a factor.

"Airbus has decided to recommend that A330/A340 operators with Thales pitot tubes, exchange at least two of them with Goodrich probes," the company said in a statement sent to the BBC.

The company said it was making the recommendation "on the basis of the very limited available information" from the Air France accident, and "despite the fact that the pitot tubes meet the certification objectives".

"This precautionary measure will allow our customers to benefit from the greater in-service experience of the Goodrich tubes on the A330/A340," it said.

The move would affect about 200 of the A330 or A340 planes which were fitted with sensors manufactured by France's Thales company, reported Reuters.

No deadline has been issued for the change to be implemented.

Earlier, the European Aviation Safety Authority (EASA) said it was to make the same recommendation.

All 228 people on board the Air France plane were killed when it plunged into the ocean en route from Rio de Janeiro to Paris on 1 June.

French investigators have said faulty speed sensors were "a factor but not the cause" of the crash.

In the wake of the crash, Air France accelerated an existing programme to replace speed monitors on its Airbus planes.

atemu - Sensor Network Emulator / Simulator / Debugger

2009-09-01T18:25:00.001-07:00

Atemu is a software emulator for AVR processor based systems. Along with support for the AVR processor, it also includes support for other peripheral devices on the MICA2 sensor node platform such as the radio. Atemu can be used to perform high fidelity large scale sensor network emulation studies in a controlled environment. In addition, the atemu package can also be used in an educational environment to facilitate experimentation with sensor networks, without requiring the purchase of expensive sensor node hardware. It also offers a solution around the logistical difficulties of conducting experiments with large numbers of physical devices. Atemu can be directly used by developers of TinyOS related software as it is binary compatible with the MICA2 hardware. Though the current release only includes support for MICA2 hardware, it can be easily extended to include other sensor node platforms. It allows for the use of heterogeneous sensor nodes in the same sensor network. The atemu distribution consists of two components: the atemu emulator core, and the xatdb graphical debugger.

The atemu emulator core can simulate arbitrary numbers of nodes and can model their execution and interactions between them, such as radio communications in extremely fine detail. It offers nearly complete emulation of the MICA2 hardware platform and as a result provides results that are closer to real life operation of a distributed sensor network. The only difference between running an actual network of the MICA2 sensor nodes and emulating it in atemu is the operation of the "air". Currently only d^2 propagation is modelled, however this will be improved with future releases. Atemu uses the same binary that is loaded onto the MICA2 node and uses its AVR processor emulation engine to very accurately model the execution of the code on each sensor node. Therefore, as very few details of the actual operation of a sensor node are abstracted out, it provides an excellent platform to perform unbiased comparisions of various sensor networking protocols and the results are significantly more realistic than other simulators.

Included in the atemu distribution is xatdb, Xatdb is a graphical gdb-like frontend to the atemu sensor network emulator. Xatdb provides users a complete system for debugging and monitoring the execution of their code. Using xatdb, users can run code built for the MICA2 platform, and debug efficiently using the ability to set breakpoints, watchpoints, as well the ability to single step through either assembly or nesC code. Xatdb is particularly powerful in its ability to provide a debugging interface to multiple nodes in a sensor network.

Major Features

Complete emulation of the AVR instruction set.
Partial support for all MICA2 board components
Loading of ELF executables and Motorola SREC images.
Gdb-like graphical debugging environment: Xatdb
Support for arbitrary numbers of breakpoints and memory watchpoints.
Support for multiple sensor nodes in a sensor network
Symbolic debugging support including source-level stepping and run-time variable inspection for programs compiled in the ELF format.
Highly configurable and modular hardware support allows for other hardware platform to be supported in the future.
Ability to run TinyOS based code
Different sensor nodes can run different programs
Support for multiple platforms including Solaris and several distributions of Linux

HyperScan

2009-08-29T19:18:00.000-07:00

HyperScan is a pattern matching engine that performs Deep Packet Inspection (DPI) and Regular Expression (Regex) matching on network traffic. Supporting millions of simultaneous patterns and matches concurrently, this high-speed software engine uses advanced knowledge of CPU caches and SIMD instructions sets to achieve up to 30Gbps of content scanning performance. HyperScan is also optimized to keep the relevant data in L1 and L2 cache to ensure maximum operational efficiency.

HyperScan is an easy to integrate software engine intended for networking and security products (appliance, server blades, switches, routers etc) that are required to perform content inspection. Technical documentation relating to integration and testing is available for all qualified customers interested in evaluating the product.

What's Unique About HyperScan

HyperScan is the fastest and most comprehensive pattern matching and acceleration software library of it’s kind. In addition to serving as a complete drop-in replacement to libPCRE (a widely used matcher for Perl-compatible regular expressions), HyperScan provides the following unique differentiators:

Up to 663X faster than libCPRE even on extremely complex patterns
Low latency performance, especially compared to hardware pattern matchers
Better scanning algorithms - underlying architecture and technology is significantly better than published state-of-the-art literature
Multicore CPU ready and highly scalable (low-end to high-end)
Very easy to integrate and portable
Utilizes advanced compilation technology to ensure small memory footprint for large signature databases
Supports streaming
- Allows matching across multiple data writes to a stream
- Needs only a small, constant-sized stream record.
Robust and Expressive
- Can use a wide range of regular expression constructs (libpcre syntax).
- No 'combinatorial explosion', no backtracking.
Very easy to Integrate and Portable
- Works on 32-bit and 64-bit systems
- OS independent
- Operates on multiple CPU architectures: Intel, Cavium/MIPS, RMI/MIPS

HyperScan Performance

HyperScan is tested and benchmarked using a wide range of signatures (patterns) extending from widely used open source databases to signatures to tier-1 vendors (IPS, AV, Content Filtering). The following diagram shows some of these results on a variety of platforms and core numbers, to demonstrate the linear scalability of the HyperScan solution.

National Instruments Introduces Wireless Sensor Network Platform

2009-08-26T17:04:00.000-07:00

New Reliable, Low-Power Wireless Measurement Nodes and NI LabVIEW Deliver Ideal Platform for Remote Monitoring Applications

NEWS RELEASE – Aug. 4, 2009 – National Instruments today announced the NI wireless sensor network (WSN) platform, a complete remote monitoring solution that consists of NI LabVIEW graphical programming software and new reliable, low-power wireless measurement nodes. The adoption of wireless technology for remote monitoring applications is growing, yet engineers and scientists struggle to find an integrated solution that can provide the required measurement quality, power management and reliable hardware for long-term, remote deployments. The NI WSN platform takes advantage of more than 30 years of NI data acquisition system leadership to deliver an easy-to-use solution that provides high-quality measurement data, the flexibility to manage power consumption and the ability to customize wireless hardware for added functionality. A key differentiator of the platform is LabVIEW software, which integrates seamlessly with the new battery-powered, industrial-rated NI WSN measurement nodes that can be deployed in rugged conditions for long periods of time.

Engineers and scientists worldwide are adopting wireless technology to meet distributed and portable measurement applications challenges, such as structural health and environmental monitoring, where wiring is difficult or cost-prohibitive. With the flexibility of LabVIEW, the NI WSN platform simplifies and accelerates the development of these applications by delivering a drag-and-drop programming environment for configuring wireless systems, extracting measurements, performing analysis and presenting data. LabVIEW also offers native Web connectivity for remote interaction with wireless systems.

“The NI WSN platform provides the ease-of-use necessary to quickly configure and deploy wireless sensors in a wide range of applications,” said Dr. William Kaiser, director of the Actuated, Sensing, Coordinated and Embedded Networked Technologies lab at UCLA. “The Center for Embedded Network Systems at UCLA is actively deploying NI WSN sensors in a parking garage at the Ronald Reagan Medical Center to help patients and family quickly identify open parking locations and to research options for proactive communication to commuters on parking availability across campus. The use of NI technology will allow us to improve the commuter experience, reduce additional traffic and emissions as commuters search for parking."

National Instruments is releasing its first two WSN nodes and plans to expand the measurement capabilities of the NI WSN platform. The wireless measurement nodes are powered by four AA batteries for up to three years, making them ideal for long-term deployments. The NI WSN-3202 four-channel, ± 10 V analog input node and NI WSN-3212 four-channel, 24-bit thermocouple node have four digital I/O channels that can be configured for input, sinking output or sourcing output. The platform also includes the NI WSN-9791 Ethernet gateway, which is used to connect the measurement nodes to LabVIEW.

The wireless devices include NI-WSN software, which connects the NI wireless devices to LabVIEW software running on Microsoft Windows or a LabVIEW Real-Time host controller. NI-WSN software is based on IEEE 802.15.4 technology and gathers measurement data from the distributed measurement nodes. The software also delivers capabilities for mesh routing and managing power usage across the network, making it possible to increase measurement distance while maintaining network reliability. Additionally, LabVIEW delivers seamless integration with wired measurement devices and a wide range of third-party wireless sensor network platforms.

While the measurement nodes are optimized for low-power, multiyear deployment with limited computing resources, LabVIEW provides the ability to customize the embedded software on each node using the LabVIEW Wireless Sensor Network Module Pioneer. Programming customized logic on traditional wireless sensor network platforms often requires expertise in embedded operating systems and low-level, event-based programming. Using the intuitive graphical programming of LabVIEW, engineers and scientists easily can program the nodes to extend battery life, perform custom analysis and reduce response time with embedded decision making.

Readers can visit www.ni.com/wsn to evaluate the technology and purchase a starter kit for the NI wireless sensor network platform. To view product availability by country, readers can visit www.ni.com/wirelesscertifications.

About National Instruments
National Instruments (www.ni.com) is transforming the way engineers and scientists design, prototype and deploy systems for measurement, automation and embedded applications. NI empowers customers with off-the-shelf software such as NI LabVIEW and modular cost-effective hardware, and sells to a broad base of more than 30,000 different companies worldwide, with no one customer representing more than 3 percent of revenue and no one industry representing more than 15 percent of revenue. Headquartered in Austin, Texas, NI has more than 5,000 employees and direct operations in more than 40 countries. For the past 10 years, FORTUNE magazine has named NI one of the 100 best companies to work for in America.

Simulating TinyOS Networks

2009-08-24T17:06:00.000-07:00

Overview

Sensor networks are composed of large numbers of tiny sensing and computing devices. Each of these devices, called motes, have very limited communication, computational and energy resources. Often embedded in uncontrolled physical environments, such as nature reserves, vineyards, or seismically threatened structures, these networks require distributed algorithms for efficient data processing, while individual motes require highly concurrent and reactive behavior for efficient operation. Their embedded nature makes controlled experiments difficult; separating the algorithmic and the real-world components of performance is almost impossible, and reproducibility is not much easier. Application development is complicated by the motes' small form factor and limited accessibility in the field; inspecting the internal state of programs on many remote nodes is laborious. Inspection that disturbs the reactive nature of a mote (e.g., a breakpoint) can invalidate the observed behavior.

We have developed a TinyOS mote simulator, TOSSIM, to ease the development of sensor network applications. TOSSIM scales to thousands of nodes, and compiles directly from TinyOS code; developers can test not only their algorithms, but also their implementations. TOSSIM simulates the TinyOS network stack at the bit level, allowing experimentation with low-level protocols in addition to top-level application systems. Users can connect to TOSSIM and interact with it using the same tools as one would for a real-world networking, making the transition between the two easy.

TOSSIM also has a GUI tool, TinyViz, which can visualize and interact with running simulations. Using an simple plugin model, users can develop new visualizations and interfaces for TinyViz.

Software

TOSSIM and its documentation are included in the standard TinyOS release.

Future

Currently, TOSSIM provides a scalable, high fidelity simulation of a complete TinyOS sensor network. Its principal limitation resides in introducing phenomena into the simulation. TinyViz allows users to interact with a simulation through a GUI, but these interactions are often ad-hoc, as well as laborious and difficult to reproduce.

We are currently developing a scripting language, as yet unnamed, which will allow users to interact with running and paused simulations. This will allow users to reproduce complex scenarios (of motion, for example) and construct tests of algorithmic corner cases. Additionally, having a full scripting language will allow users to build scripting primitives, slowly creating a library of functions and test cases for TinyOS efforts.

Initially, work on TinyOS was very low level, exploring things such as media access, sensor filtering, and timer implementations. The initial design of TOSSIM was focused on this work: it simulates every bit of the mica platform radio i stack. As this work was matured, more and more effort has been spent on higher layers, such as complex applications. In order to support developers of these larger systems, we are currently developing a packet-level simulation for the mica2 platform. The challenge is to capture all of the issues and problems that can arise in commmunication (timing, packet corruption, MAC) while remaining efficient. Initial evaluation suggests a hundred-fold performance improvement; however, this option will not support applications that modify low level radio implementations.

Mobile Ad-Hoc Network (MANET) and Sensor Network Security

2009-08-19T06:33:00.000-07:00

In 2006, our research team released an updated open source implementation of mLab, a Mobile Ad Hoc Network (MANET) test bed. This test bed allows researchers the opportunity to validate ad hoc networking theories and simulations in practice, to test simulation assumptions, and to discover practical problems facing ad hoc network users and developers alike. The mLab tool allows users to create arbitrary network topologies and traffic scenarios in order to perform real-time performance measurements of routing protocols. By changing the logical topology of the network, mLab users can conduct tests in an ad hoc network without having to physically move the nodes in the ad hoc network. The tool allows users to replay different mobility scenarios, captures wireless traffic for further analysis, and helps perform specification-based intrusion detection. The research team has published and presented the results at six international conferences.

A number of Intrusion Detection System (IDS) techniques for MANETs have been proposed in the research literature. These techniques include trust building and cluster-based voting schemes, host-based watchdogs, and finite state machines for specifying correct routing behavior. Comparing and evaluating the effectiveness of these IDS techniques has been hindered by the limited number of large-scale MANET deployments, the lack of publicly available network traces of actual MANET traffic, and the difficulty in defining typical application and mobility scenarios. Network simulation tools have allowed researchers to study MANET IDSs without purchasing mobile nodes or conducting costly and time-consuming field trial tests. These simulations, however, have been conducted using widely varying assumptions on background network traffic, mobility, previous security associations, and the type of malicious network activity. In 2007, our research team will be using the mLab test bed to create publicly available MANET network traces. These network traces will allow a broader range of researchers to compare the effectiveness of different MANET IDS techniques on the same data set, and conduct cost-effective and time-saving offline experiments with new IDS techniques without requiring expensive hardware.

Wireless Sensor Networks: Opportunities for Fundamental Research

2009-08-10T07:57:00.000-07:00

Networking unattended wireless sensors is expected to have significant impact on the efficiency of a large array of military
and non-military applications. The main goal of wireless sensor networks is to obtain globally meaningful information from strictly local gleaned by individual sensor nodes. The network is deployed such that the sensors are embedded, possibly at random, in a target environment. Utilizing the basic capabilities of sensors in the network different types of monitoring and control applications that address the target environment can be developed. Depending on the application at hand, the interface between a sensor network and the outside world is provided by aircraft, helicopters, ground-based vehicles, satellites, co-located sink-nodes, among others. Wireless sensor networks are sufficiently different from classical wired/wireless networks that a new set of protocols that take into account the fundamental limitations of sensors have to be developed. For example, in was recently noted that the ultra-lightweight protocols imposed by the stringent energy limitations may leave not much room for advanced encryption schemes. Consequently, protection against overhearing in military applications and privacy protection in personal systems needs to be inherently built into the concepts from the beginning. Reliability is expected to be a result of the large number of sensors deployed for a specific task. However, this can only be obtained if defective sensors can be excluded from the communication, and the sensors are calibrated – either individually or collectively, either before deployment or continuously in their environment. Since sensor network research is in its infancy, we are facing a unique challenge and opportunity: that of developing fundamental research for this new type of networks that promises to revolutionize the way we live and work.

Wireless Vital Sign Sensors

2009-07-17T21:48:00.000-07:00

We have developed a range of wireless medical sensors based on the popular TinyOS "mote" hardware platforms. A wireless pulse oximeter and wireless two-lead EKG were among the first two sensors developed by our lab. These devices collect heart rate (HR), oxygen saturation (SpO2), and EKG data and relay it over a short-range (100m) wireless network to any number of receiving devices, including PDAs, laptops, or ambulance-based terminals. The data can be displayed in real time and integrated into the developing pre-hospital patient care record. The sensor devices themselves can be programmed to process the vital sign data, for example, to raise an alert condition when vital signs fall outside of normal parameters. Any adverse change in patient status can then be signaled to a nearby EMT or paramedic. These vital sign sensors consist of a low-power microcontroller (Atmel Atmega128L or TI MSP430) and low-power digital spread-spectrum radio (Chipcon CC2420, compliant with IEEE 802.15.4, 2.4 GHz, approximate range 100 meters, data rate about 80 Kbps). The devices have a small amount of memory (4-10 KB) and can be programmed (using the TinyOS operating system) to sample, transmit, filter, or process vital sign data. These devices are powered by 2 AA batteries with a lifetime of up to several months if programmed appropriately. The basic hardware is based on the MicaZ and Telos sensor nodes, described above, and a custom sensor board integrating the pulse oximeter or EKG circuitry is attached to the mote devices.


Wireless pulse oximeter sensor.	Wireless two-lead EKG.	Accelerometer, gyroscope, and electromyogram (EMG) sensor for stroke patient monitoring.

CodeBlue is also being used by the AID-N project at Johns Hopkins Applied Physics Laboratory, which is investigating a range of technologies for disaster response. The AID-N wireless sensors (which run the CodeBlue software) include an electronic "triage tag" with pulse oximeter, LCD display, and LEDs indicating patient status; a packaged version of our two-led EKG mote, and a wireless blood pressure cuff. The ETag sensor hardware was developed by Leo Selavo at University of Virginia.


UVa/AID-N "eTag" wireless triage tags, with pulse oximeter, LEDs to indicate patient triage status, and control buttons.	UVa/AID-N wireless two-lead EKG (same as above, but with case).

CodeBlue: Wireless Sensors for Medical Care

2009-07-10T18:38:00.000-07:00

We are exploring applications of wireless sensor network technology to a range of medical applications, including pre-hospital and in-hospital emergency care, disaster response, and stroke patient rehabilitation (see the related Mercury project as well).

Recent advances in embedded computing systems have led to the emergence of wireless sensor networks, consisting of small, battery-powered "motes" with limited computation and radio communication capabilities. Sensor networks permit data gathering and computation to be deeply embedded in the physical environment. This technology has the potential to impact the delivery and study of resuscitative care by allowing vital signs to be automatically collected and fully integrated into the patient care record and used for real-time triage, correlation with hospital records, and long-term observation.

This project is supported by grants from the National Science Foundation, National Institutes of Health, U.S. Army, as well as generous gifts from Sun Microsystems, Microsoft Corporation, Intel Corporation, Siemens AG, and ArsLogica.

The Dream and Reality of Wireless Sensor Networks

2009-06-26T23:36:00.001-07:00

G-Link® Wireless Accelerometer Node

2009-06-22T22:38:00.000-07:00

Triaxial accelerometers combined with datalogging transceiver for use in high speed wireless sensor networks.

Features and Benefits

2.4 GHz direct sequence spread spectrum radio is license free worldwide
IEEE 802.15.4 open communication architecture
Supports simultaneous streaming from multiple nodes
Available with 2g or 10g range
Datalogging rates up to 2048 Hz, storing up to 1,000,000 measurements
Real-time streaming rates up to 4 KHz
Communication range up to 70 m line-of-sight, 300 m with high gain antennas
Low power consumption for extended use
Internal rechargeable battery

Introduction

Combining MEMS accelerometers with MicroStrain® award-winning Micro Datalogging Transceiver systems, G-Link® wireless accelerometer node is a high speed, triaxial accelerometer node, designed to operate as part of an integrated wireless sensor network system.

Featuring 2 KHz sweep rates, combined with 2 MB of flash memory, these little nodes pack a lot of power in a small package. Every node in the wireless network is assigned a unique 16 bit address, so a single host transceiver can address thousands of multichannel sensor nodes.

The bi-directional RF communications link can trigger a sample to be logged from 70 meters, or request real-time data to be transmitted to the host PC for data acquisition/analysis. The frequency agile system enables simultaneous real-time streaming from up to 16 nodes in the 2.4 GHz range.

Available with a 2g or 10g range, these small, fast, wireless accelerometers can be used to monitor tilt and vibration in a wide range of machines and structures.

A software development kit is available, which includes fully-commented source code and a compiled executable for Microsoft® Visual Studio C++ .NET 7.1, Microsoft® VB 6.0, Microsoft® VB.NET 2003, Microsoft® VB.NET 2005 and LabVIEW® 7.1.

Applications

Inclination & vibration testing, control

Security systems enabled by wireless sensor networks

Assembly line testing with "smart packaging"

Sports performance and sports medicine analysis

Condition-based maintenance by wireless sensor networks

Smart machines, smart structures & smart materials

On-board Accelerometers

Triaxial MEMS accelerometers, Analog Devices AD22293 (2g) or ADXL210 (10g)

Accelerometer Range

± 2g or ± 10g

Measurement Accuracy

10 mg

Resolution

1.5mg RMS(2g), 9mg RMS(10g)

Temperature Sensor

-40˚C to +70˚C range, accuracy 2˚C (at 25˚C)

Anti-aliasing Filter Bandwidth

-3 dB cutoff at 250 Hz (factory adjustable)

Analog to Digital (A/D) Converter

Successive approximation type, 12 bit resolution

Data Storage Capacity

2 megabytes (approximately 1,000,000 data points)

Data Logging Mode

Log up to 1,000,000 data points (from 100 to 65,500 samples or continuous) at 32 Hz to 2048 Hz

Sensor Event Driven Trigger

Commence datalogging when threshold exceeded

Real-time Streaming Mode

Transmit real time data from node to PC - rate depends on number of active channels:
1 channel - 4 KHz,
2 channels - 2 KHz,
3 channels - 1.33 KHz,
4 channels (including temp.) - 1 KHz

Low Duty-cycle Mode

Supports multiple nodes on single RF channel

Synchronization Between Nodes

Datalogging 4 sec 50 ppm, LDC mode time stamped at PC

Sample Rate Stability

25 ppm for sample rates > 1 Hz, 10% for sample rates <>

Radio Frequency (RF)Transceiver Carrier

2.4 GHz direct sequence spread spectrum, license free worldwide (2.405 to 2.480 GHz) - 16 channels, radiated power 0 dBm (1 mW)

RF Data Packet Standard

IEEE 802.15.4, open communication architecture

RF Data Downloading

8 minutes to download full memory

Range for Bi-Directional RF Link

70 m line-of-sight, up to 300 m with optional high gain antenna

Internal Li-Ion Battery

3.7 volt lithium ion rechargeable battery, 250 mAh capacity
Customer may supply external power from 3.2 to 9 volts

Power Consumption

Real-time streaming - 25 mA, datalogging - 25 mA, sleeping - 0.5 mA

Operating Temperature

-20˚C to +60˚C with standard internal battery and enclosure, extended temperature range optional with custom battery and enclosure. -40˚C to +85˚C for electronics only

Maximum Acceleration Limit

500 g

Dimensions

58 mm x 43 mm x 26 mm (enclosure without antenna)
36 mm x 36 mm x 24 mm (circuit board assembly only)
For dimensioned print go to Documentation tab

Weight

47 g (with enclosure)
15 g (circuit board assembly only)

Enclosure Material

ABS plastic

Software

Node Commander® Windows XP/Vista compatible

Compatible Base Stations

USB, RS-232, Analog, WSDA®

Sensor Networks, New Face of Computing

2009-06-15T22:29:00.000-07:00

Wireless sensor networks have rapidly emerged as a technology that can bring great benefits to all aspects of our life and society, ranging from home to environment and from healthcare to national security and defence. This has been possible due to a confluence of recent advances in wireless communication, VLSI design and micro-fabrications, and low-cost low-power embedded microprocessor systems.

A sensor network typically consists of a large number of small, inexpensive sensor nodes, each endowed with sensing, computing, and communication capabilities.

These nodes talk to each other and/or to one or more servers where the data may be integrated, fused, analyzed and made available to other systems and human operators.

Sensor networks, thus, extend the Internet into the physical world. The sensors collect information about the world-it may be physical parameters such as temperature, humidity, water level and pressure, atmospheric moisture content, and soil motion and vibrations; or chemical sensors that assess the content of soil, water, and air; or it may be an imaging or video sensor; or it might just sense the presence and identity of the object. There is no inherent restriction on what property may be sensed-anything that is appropriate for the particular application at hand is valid. The power of sensor networks comes from the fact that a large number of sensors are coupled to a vast source of real-world knowledge. Ultimately, this technology has the potential to seamlessly merge the physical world and information world to bring the power of global knowledge for managing local events and precise local data to global analysis.

The potential benefits of sensor networks for our life and society are, thus, immense. In a country like India, which is prone to natural disasters such as floods, landslides, cyclonic storms and earthquakes, this technology offers a way to continuously monitor the environment and give us early warnings of impending catastrophic events. For instance, Microsoft Research India is collaborating with Prof U B Desai at IIT Bombay who is working on a project aimed at landslide detection. His team's goal is to monitor the earth in landslide prone areas to look for tiny vibrations and movements in the rock and soil over a large area. The data from a distributed area can be fused, integrated and analyzed together with known geographical and seismic properties of that area to predict impending landslides. Similarly, Prof B N Jain of IIT Delhi is exploring the use of sensor networks in the context of other natural events such as floods.

However, research in this field is still at a stage of infancy; to successfully realize these vast benefits, a core set of technical problems has to be solved. These include development of small, low cost sensors, appropriate communication systems and networking protocols, a programming environment for developing applications on the sensor networks, database and visualization capabilities that will exploit the data and provide the user access to the information in a relevant and succinct fashion.

The development of small, inexpensive sensors, with reasonable computing and communication capabilities has been a major driver of this research area. As in the case of other electronics and VLSI technology, there have been continuous improvements in the performance and power of sensor nodes, while the cost and size of the units have reduced.

One of the most vibrant research areas today relates to communication in wireless sensor networks. Many traditional communication protocols used in wireless and cellular networking do not automatically apply to sensor networks for several reasons. For instance, sensor nodes have limited power and energy capacity which limit the distance over which each node may be able to transmit and receive information. Traditional methods used in cellular and other networks that use a base station and dedicated resource assignment strategies will not be suitable because there is usually no single central controlling node. A natural approach may be to consider ad-hoc mesh networking methods. However, sensors are not often robust or rugged and in many cases may be lost or destroyed by natural forces.
This has to be factored into the communication protocols. Various protocols are now being developed for communication and routing of information in sensor networks, of which many are adaptations of existing wireless and cellular protocols that handle the specific energy, power, noise and reliability issues associated with sensor networks.

Images of the very compact Berkeley Motes, small sensor devices, developed by Berkeley University

Another important aspect of sensor network research relates to programming and developing sensor network applications. One of the more widely used environments is TinyOS, an event-based operating environment originally developed by University of California, Berkeley. It is designed to support the concurrency -intensive operations typical of sensor nodes. Programming a network of sensors is inherently challenging, because unlike existing platforms, systems such as sensor networks are decentralized, embedded in the physical world, and interact with people. In addition the programming environments have to account for node uncertainty, power and energy constraints and even for inter node collaboration. Research on this topic is still in its early stages. While there are some tools aimed at programming individual nodes, the development of large-scale applications that run across a network of such nodes require new programming paradigms, tools and platforms. A few groups are exploring the issue of how to program sensor networks. For example, the networked-embedded computing group at Microsoft Research, Redmond USA (led by Dr Feng Zhao) is developing new architectures, models and tools for organizing and programming these systems.

Ultimately, to realize the power of sensor networks, we need the fusion of integration of the sensed data together with contextual knowledge. The existing database and storage technologies require considerable adaptation to handle sensor network scenarios where data is inherently distributed and changing dynamically in an unpredictable and ad-hoc fashion. The information needed will extend beyond the information available at single nodes or even a set of nodes as data from other geographically distributed sensors will have to be integrated and fused. Queries will be at a much higher level than the data and will have to be parsed into queries about a diverse variety of sensors. The data analysis, fusion, and integration algorithms and the database will have to be designed together and the entire system has to function robustly, given unreliable nodes and ad-hoc connectivity. This poses new research challenges that extend beyond conventional database design.

Despite the challenges, the field of sensor networks is one of the most exciting emerging research areas. These networks have vast potential to revolutionize the way we monitor and live in our environments. However, the discipline is still young, the technologies are still fresh, and it may be some time before we can realize the full benefits of this new and emerging computing paradigm.

G-Link® Wireless Accelerometer Node

2009-06-10T20:17:00.000-07:00

Triaxial accelerometers combined with datalogging transceiver for use in high speed wireless sensor networks.

Features and Benefits

2.4 GHz direct sequence spread spectrum radio is license free worldwide
IEEE 802.15.4 open communication architecture
Supports simultaneous streaming from multiple nodes
Available with 2g or 10g range
Datalogging rates up to 2048 Hz, storing up to 1,000,000 measurements
Real-time streaming rates up to 4 KHz
Communication range up to 70 m line-of-sight, 300 m with high gain antennas
Low power consumption for extended use
Internal rechargeable battery

Introduction

Available with a 2g or 10g range, these small, fast, wireless accelerometers can be used to monitor tilt and vibration in a wide range of machines and structures.

Applications

Inclination & vibration testing, control
Security systems enabled by wireless sensor networks
Assembly line testing with "smart packaging"
Sports performance and sports medicine analysis
Condition-based maintenance by wireless sensor networks
Smart machines, smart structures & smart materials

On-board Accelerometers

Triaxial MEMS accelerometers, Analog Devices AD22293 (2g) or ADXL210 (10g)

Accelerometer Range

± 2g or ± 10g

Measurement Accuracy

10 mg

Resolution

1.5mg RMS(2g), 9mg RMS(10g)

Temperature Sensor

-40˚C to +70˚C range, accuracy 2˚C (at 25˚C)

Anti-aliasing Filter Bandwidth

-3 dB cutoff at 250 Hz (factory adjustable)

Analog to Digital (A/D) Converter

Successive approximation type, 12 bit resolution

Data Storage Capacity

2 megabytes (approximately 1,000,000 data points)

Data Logging Mode

Log up to 1,000,000 data points (from 100 to 65,500 samples or continuous) at 32 Hz to 2048 Hz

Sensor Event Driven Trigger

Commence datalogging when threshold exceeded

Real-time Streaming Mode

Transmit real time data from node to PC - rate depends on number of active channels:
1 channel - 4 KHz,
2 channels - 2 KHz,
3 channels - 1.33 KHz,
4 channels (including temp.) - 1 KHz

Low Duty-cycle Mode

Supports multiple nodes on single RF channel

Synchronization Between Nodes

Datalogging 4 sec 50 ppm, LDC mode time stamped at PC

Sample Rate Stability

25 ppm for sample rates > 1 Hz, 10% for sample rates <>

Radio Frequency (RF)Transceiver Carrier

2.4 GHz direct sequence spread spectrum, license free worldwide (2.405 to 2.480 GHz) - 16 channels, radiated power 0 dBm (1 mW)

RF Data Packet Standard

IEEE 802.15.4, open communication architecture

RF Data Downloading

8 minutes to download full memory

Range for Bi-Directional RF Link

70 m line-of-sight, up to 300 m with optional high gain antenna

Internal Li-Ion Battery

3.7 volt lithium ion rechargeable battery, 250 mAh capacity
Customer may supply external power from 3.2 to 9 volts

Power Consumption

Real-time streaming - 25 mA, datalogging - 25 mA, sleeping - 0.5 mA

Operating Temperature

-20˚C to +60˚C with standard internal battery and enclosure, extended temperature range optional with custom battery and enclosure. -40˚C to +85˚C for electronics only

Maximum Acceleration Limit

500 g

Dimensions

58 mm x 43 mm x 26 mm (enclosure without antenna)
36 mm x 36 mm x 24 mm (circuit board assembly only)
For dimensioned print go to Documentation tab

Weight

47 g (with enclosure)
15 g (circuit board assembly only)

Enclosure Material

ABS plastic

Software

Node Commander® Windows XP/Vista compatible

Compatible Base Stations

USB, RS-232, Analog, WSDA®

Solar Powered, Ruggedized Wireless Sensor Nodes Solar Powered, Ruggedized Wireless Sensor Nodes

2009-06-06T20:21:00.000-07:00

Features and Benefits

2.4 GHz direct sequence spread spectrum radio is license free worldwide
IEEE 802.15.4 open communication architecture
Supports simultaneous streaming from multiple nodes to PC
Datalogging rates up to 2048 Hz, stores up to 1,000,000 measurements
Real-time streaming rates up to 4 KHz
Communication range up to 70 m line-of-sight, 300 m with high gain antennas
Rugged NEMA 4X enables long-term exposure in hostile environment
Low power consumption for extended use with primary batteries or solar recharged lithium batteries
Internal rechargeable battery

Introduction

MicroStrain® Field Ruggedized Wireless Sensor Nodes contain either a V-Link® wireless voltage node, SG-Link® wireless strain node, G-Link® wireless accelerometer node, or TC-Link® wireless thermocuple node. Each is housed in a rugged NEMA 4X enclosure designed for long-term exposure to the elements. These rugged nodes come standard with high capacity lithium batteries, designed for operation in extreme temperatures. Optionally, they can be run with solar power, providing autonomous operation for many years.

The V-Link® wireless voltage node and SG-Link® can be used with most types of analog sensors including: strain gauges, displacement sensors, load cells, pressure sensors, settlement systems, accelerometers, geophones, temperature sensors and others. The G-Link® wireless accelerometer node incorporates a triaxial 2 g or 10 g accelerometer. The TC-Link® wireless thermocouple node supports 6 thermocouples and a relative humidity sensor.

Applications

Condition-based monitoring of machines
Health monitoring of civil structures
Smart structures and materials
Experimental test and measurement
Vibration and acoustic noise testing
Distributed security networks

Common Specifications

(excluding TC-Link®)

Temperature sensor

-40˚C to 70˚C range, accuracy 2˚C (at 25˚C)

Analog to Digital (A/D) Converter

Successive approximation type, 12 bit resolution

Data Storage Capacity

2 megabytes (approximately 1,000,000 data points)

Data Logging Mode

Log up to 1,000,000 data points (from 100 to 65,500 samples or continuous) at 32 Hz to 2048 Hz

Sensor Event Driven Trigger

Commence datalogging when threshold exceeded

Real-time Streaming Mode

Transmit real time data from node to PC - rate depends on number of active channels: 1 channel - 4 KHz, 2 channels - 2 KHz, 3 channels - 1.33 KHz, 4 channels - 1 KHz, 5 channels - 800 Hz, 6 channels - 666 Hz, 7 channels - 570 Hz, 8 channels - 500 Hz

Low Duty-cycle Mode

Supports multiple nodes on single RF channel

Synchronization Between Nodes

Datalogging 4 sec 50 ppm, LDC mode time stamped at PC

Sample Rate Stability

25 ppm for sample rates > 1 Hz, 10% for sample rates <>

Radio Frequency (RF) Transceiver Carrier

2.4 GHz direct sequence spread spectrum, license free worldwide (2.405 to 2.480 GHz) - 16 channels, radiated power 0 dBm (1mW)

RF Data Packet Standard

IEEE 802.15.4, open communication architecture

RF Data Downloading

8 minutes to download full memory

Range for Bi-directional RF Link

70 m line-of-sight, up to 300 m with optional high gain antenna

Internal Li-Ion Battery

3.7 volt lithium ion rechargeable battery
Customer may supply external power from 3.2 to 9 volts

Autonomy

Depends upon sensors and power source. Solar powered nodes can be designed to operate for up to 5 years without battery maintenance

Operating Temperature

-20˚C to +60˚C with standard battery
-20˚C to +60˚C with rechargeable battery for solar power
-40˚C to +85˚C for electronics only

Mounting Options

Magnetic, clamp, bolt or pipe mountable

Dimensions

140 mm x 120 mm x 100 mm without solar panel and mounting hardware

Enclosure Material

ABS plastic, NEMA 4X

Software

Node Commander® Windows XP/Vista compatible

Compatible Base Stations

USB, RS-232, Analog, WSDA®

Patents pending

Wireless Sensor Network Security - A Survey - 6

2009-06-04T21:44:00.000-07:00

6.7 Defending Against Attacks on Sensor Privacy

Regarding the attacks on privacy mentioned earlier, there exist effective
techniques to counter many of the attacks levied against a sensor. Here we
describe several common techniques [28].

6.7.1 Anonymity Mechanisms

Location information that is too precise can enable the identification of a
user, or make the continued tracking of movements feasible. This is a threat
to privacy. Anonymity mechanisms depersonalize the data before the data
is released, which present an alternative to privacy policy-based access control.
Researchers have discussed several approaches using anonymity mechanisms,
for example, Gruteser and Grunwald [26] analyze the feasibility
of anonymizing location information for location-based services in an automotive
telematics environment; Beresford and Stajano [6] independently
evaluate anonymity techniques for an indoor location system based on the
Active Bat.

Total anonymity is a difficult problem given the lack of knowledge concerning
a node’s location. Therefore, a tradeoff is required between anonymity

and the need for public information when solving the privacy problem.
In [27, 28, 67, 76], three main approaches are proposed:
• Decentralize Sensitive Data The basic idea of this approach is to
distribute the sensed location data through a spanning tree, so that
no single node holds a complete view of the original data.
• Secure Communication Channel Using secure communication protocols,
such as SPINS [65], the eavesdropping and active attacks can
be prevented.
• Change Data Traffic De-patterning the data transmissions can protect
against traffic analysis. For example, inserting some bogus data
can intensively change the traffic pattern when needed.
• Node Mobility Making the sensor movable can be effective in defending
privacy, especially the location. For example, the Cricket system
[67] is a location-support system for in-building, mobile, location
dependent applications. It allows applications running on mobile and
static nodes to learn their physical location by using listeners that hear
and analyze information from beacons spread throughout the building.
Thus the location sensors can be placed on the mobile device
as opposed to the building infrastructure, and the location information
is not disclosed during the position determination process and the
data subject can choose the parties to which the information should
be transmitted.

6.7.2 Policy-based Approaches

Policy-based approaches are currently a hot approach to address the privacy
problem. The access control decisions and authentication are made based
on the specifications of the privacy policies. In [57], Molnar and Wagner
present the concept of private authentication, and give a general scheme for
building private authentication with work logarithmic in the number of tags
in (but not limited by) RFID (radio frequency identification) applications.
In the automotive telematics domain, Duri and colleagues [20] propose a
policy-based framework for protecting sensor information, where an in-car
computer can act as a trusted agent. Snekkenes [77] presents advanced concepts
for specifying policies in the context of a mobile phone network. These
concepts enable access control based on criteria such as time of the request,
location, speed, and identity of the located object. Myles and colleagues [58]

describe an architecture for a centralized location server that controls access
from client applications through a set of validator modules that check
XML-encoded application privacy policies. Hengartner and Steenkiste [31]
point out that access control decisions can be governed by either room or
user policies. The room policy specifies who is permitted to find out about
the people currently in a room, while the user policy states who is allowed
to get location information about another user.
6.7.3 Information Flooding
Ozturk et al. propose anti-traffic analysis mechanisms to prevent an outside
attacker from tracking the location of a data source, since that information
will release the location of sensed objects [61]. The randomized data routing
mechanism and phantom traffic generation mechanism are used to disguise
the real data traffic, so that it is difficult for an adversary to track the
source of data by analyzing network traffic. Based on flooding-based routing
protocols, Ozturk et al. have developed comparable methods for single path
routing to try to solve the privacy problems in sensor network.
• Baseline Flooding In the baseline implementation of flooding, every
node in the network only forwards a message once, and no node
retransmits a message that it has previously transmitted. When a
message reaches an intermediate node, the node first checks whether
it has received and forwarded that message before. If this is its first
time,the node will broadcast the message to all its neighbors. Otherwise,
it just discards the message.
• Probabilistic Flooding In probabilistic flooding, only a subset of
nodes within the entire network will participate in data forwarding,
while the others simply discard the messages they receive. One possible
weakness of this approach is that some messages may get lost
in the network and as a result affect the overall network connectivity.
However, as [61] explain later in this section, this problem does not
appear to be a significant factor.
• Flooding with Fake Messages The previous flooding strategies can
only decrease the chances of a privacy violation. An adversary still
has a chance to monitor the general traffic and even the individual
packets. This observation suggests that one approach to alleviate the
risk of source-location privacy breaching is to augment the flooding
31
protocols to introduce more sources that inject fake messages into the
network. By doing so, even if the attacker captures the packets, he
will have no idea whether the packets are real.
• Phantom Flooding Phantom flooding shares the same insights as
probabilistic flooding in that they both attempt to direct messages to
different locations of the network so that the adversary cannot receive
a steady stream of messages to track the source. Probabilistic flooding
is not very effective in achieving this goal because shorter paths are
more likely to deliver more messages. Therefore, Ozturk et al. [61]
suggest enticing the attacker away from the real source and towards
a fake source, called the phantom source. In phantom flooding, every
message experiences two phases: (1) a walking phase,which may be a
random walk or a directed walk, and (2) a subsequent flooding meant
to deliver the message to the sink. When the source sends out a
message, the message is unicast in a random fashion within the first
hwalk hops (referred to as random walk phase). After the hwalk hops,
the message is flooded using the baseline flooding technique (referred
to as flooding phase).
Similar mechanisms are also used to disguise an adversary from finding the
location of a base station by analyzing network traffic [29]. One key problem
for these anti-traffic analysis mechanisms is the energy cost incurred by
anonymization.

Another strategy used to mask location information from eavesdroppers
is presented in [89]. They propose a two way greedy random-walk strategy
GROW (Greedy Random Walk). In this case, the random walk is taken
from both the source and the sink. The sink first initiates a N-hop random
walk. The source then initiates a M-hop random walk. Once the source
packet reaches an intersection of these two paths, it is forwarded through
the path created by the sink. Local broadcasting is used to detect when
the two paths intersect. In order to minimize the chance of backtracking
along the random walk, the nodes are stored in a bloom filter as the walk
progresses. At each stage, the intermediate nodes are checked against the
bloom filter to ensure that backtracking is minimized [89].
6.8 Intrusion Detection
We now turn to the area of intrusion detection in wireless sensor networks.
It is important to note that in this section we cover intrusion detection as

it applies to detecting attacks on the sensor network itself, rather than the
popular intrusion detection application being researched for such uses as
perimeter monitoring, and so forth.
With that in mind, we note that intrusion detection is not necessarily
a category unto itself, but rather has its place in nearly every aspect of
sensor network security. Many secure routing schemes attempt to identify
network intruders, and key establishment techniques are used in part to
prevent intruders from overhearing network data.
Despite the necessity of effective intrusion detection schemes for wireless
sensor networks, a good solution has not yet been devised. Of course, this is
due largely to the resource constraints present in wireless sensor networks.
However, resource constraints are not the only reason. Another problem
is that researchers have not yet been able to develop methods of reliably
detecting intruders in sensor networks. As such, it is difficult to define
characteristics (or signatures) that are specific to a network intrusion as
opposed to the normal network traffic that might occur as the result of
normal network operations or malfunctions resulting from the environment
change.

6.8.1 Background on Intrusion Detection

Traditionally, intrusion detection has focused on two major categories: anomaly
based intrusion detection (AID), and misuse intrusion detection (MID) [72].
Anomaly based intrusion detection relies on the assumption that intruders
will demonstrate abnormal behavior relative to the legitimate nodes. Thus,
the object of anomaly based detection is to detect intrusion based on unusual
system behavior. Typically this is done by first developing a profile
of the system in normal use. Once the profile has been generated it can be
used to evaluate the system in the face of intruders.

The advantage of using an anomaly based system is that it is able to
detect previously unknown attacks based only upon knowing that the system
behavior is unusual. This is particularly advantageous in wireless sensor
networks where it can be difficult to boil an attack down to a signature.
However, such flexible intrusion detection comes at a cost. The first is that
the anomaly based approach is susceptible to false positives. This is due
largely to the fact that it can be difficult to define normal system behaviors.
To help combat this, new profiles can be taken of the network to ensure that
the profile in use is up-to-date. However, this takes time. And further, even
with the most up-to-date profile possible, it can still be difficult to discern

unusual, but legitimate, behavior from an actual intrusion. Another fault in
the anomaly based intrusion detection techniques is that the computational
cost of comparing the current system activity to the profile can be quite
high [72]. In the case of a wireless sensor network, such added computation
can severely impact the longevity of the network.
In systems based on misuse intrusion detection, the system maintains
a database of intrusion signatures. Using these signatures, the system can
easily detect intrusions on the network. Further, the system is less prone
to false positives as the intrusion signatures are narrowly defined. Such
narrowly defined signatures, while leading to fewer false positives, also imply
that the intrusion detection system will be unable to detect unknown
attacks. This problem can be somewhat mitigated by maintaining an up-todate
signature database. However, since it can be difficult to characterize
attacks on wireless sensor networks, such databases may be inherently limited
and difficult to generate. An advantage, however, is that the misuse
intrusion detection system requires less computation in order to identify intruders
as the comparison of network events to the available signatures is
relatively low cost [72].

Because both techniques have their strengths and weaknesses, traditional
intrusion detection systems use systems that implement both anomaly based
intrusion detection and misuse intrusion detection models. This allows such
systems to utilize the fast evaluation of the misuse intrusion detection system,
but still recognize abnormal system behavior.

6.8.2 Intrusion Detection in Wireless Sensor Networks

Typically a wireless sensor network uses cryptography to secure itself against
unauthorized external nodes gaining entry into the network. But cryptography
can only protect the network against the external nodes and does little
to thwart malicious nodes that already possess one or more keys. Brutch and
Ko classify intrusion detection systems (IDS) into two categories: host-based
and network-based. They further classify intrusion detection schemes into
those that are signature based, anomaly based, and specification based [9].
Simply put, a host based IDS system operates on operating systems audit
trails, system call audit trails, logs, and so on. A network based IDS, on the
other hand, operates entirely on packets that have been captured from the
network [9]. A signature based IDS simply monitors the network for specific
pre-determined signatures that are indicative of an intrusion. In an anomaly
based scheme, a standard behavior is defined and any deviation from that

behavior triggers the intrusion detection system. Finally, a specification
based scheme defines a set of constraints that are indicative of a program’s
or protocol’s correct operation [9].
Brutch and Ko describe a series of attacks against several aspects of a
wireless sensor network and also introduce three architectures for intrusion
detection in wireless sensor networks. The first is termed the stand-alone
architecture. In this case, as its name implies, each node functions as an
independent intrusion detection system and is responsible for detecting attacks
directed toward itself. Nodes do not cooperate in any way [9].
The second architecture is the distributed and cooperative architecture.
In this case, an intrusion detection agent still resides on each node (as in
the case of the stand-alone architecture) and nodes are still responsible for
detecting attacks against themselves (local attacks), but also cooperate to
share information in order to detect global intrusion attempts [9].
The third technique proposed by Brutch and Ko is called the hierarchical
architecture. These architectures are suitable for multi-layered wireless
sensor networks. In this case, Brutch and Ko describe a multi-layered network
as one in which the network is divided into clusters with cluster-head
nodes responsible for routing within the cluster. The multi-layered network
is used primarily for event correlation.
Albers et al. describe an intrusion detection architecture based on the

implementation of a local intrusion detection system (LIDS) at each node [2].
In order to extend each node’s “vision” of the network, Albers suggests that
the LIDS existing within the network should collaborate with one another.
All LIDS within the network will exchange two types of data, security data
and intrusion alerts. The security data is simply used to exchange information
with other network hosts. The intrusion alerts, however, are used to
inform other LIDS of a locally detected intrusion [2].

A pictorial representation of the LIDS architecture is depicted in Figure
2. MIB (management information base) variables are accessed through
SNMP running on the mobile host, where the LIDS components are depicted
within the block labeled LIDS. The local MIB is designed to interface with
the SNMP agent to provide MIB variable collection from the local LIDS
agent or mobile agents. The mobile agents are responsible for both the
collection and processing of data from remote hosts, specifically SNMP requests.
The agents are capable of migration between individual hosts and
are capable of transferring data back to their home LIDS. The local LIDS
agent is responsible for detecting and responding to local intrusions as well
as responding to events generated by remote nodes [2].

Albers et al. propose to use SNMP auditing as the audit source for each
LIDS. Rather than simply sending the SNMP messages over an unreliable
UDP connection, it is suggested that mobile agents will be responsible for
message transporting. In order to detect an intrusion, Albers suggests using
either misuse or anomaly detection. When a LIDS detects an intrusion, it
should communicate this intrusion to other LIDS on the network. Possible
responses include forcing the potential intruder to re-authenticate, or to
simply ignore the suspicious node when performing cooperative actions [2].
Although this approach can not be applied to wireless sensor network directly,
it is an interesting idea that explores the local information only, which
is the key to any intrusion detection techniques in sensor network [22]. In
summary, we envision that the intrusion detection in wireless sensors remains
an open problem, and more study is needed. Taking the pre-deployment information,
such as sensing data distribution, into consideration is a possible
direction.

6.9 Secure Data Aggregation

As wireless sensor networks continue to grow in size, so does the amount
of data that the sensor networks are capable of sensing. However, due to
the computational constraints placed on individual sensors, a single sensor

is typically responsible for only a small part of the overall data. Because of
this, a query of the wireless sensor network is likely to return a great deal
of raw data, much of which is not of interest to the individual performing
the query.

Thus, it is advantageous for the raw data to first be processed so that
more meaningful data can be gleaned from the network. This is typically
done using a series of aggregators. An aggregator is responsible for collecting
the raw data from a subset of nodes and processing/aggregating the raw data
from the nodes into more usable data.
However, such a technique is particularly vulnerable to attacks as a single
node is used to aggregate multiple data. Because of this, secure information
aggregation techniques are needed in wireless sensor networks where one or
more nodes may be malicious.

6.9.1 Introduction to Data Aggregation and Its Utility

Before discussing the security aspects of secure information aggregation, we
first begin with an overview of several information aggregating techniques.
Clustering techniques are discussed in [22]. They develop a localized algorithm
that uses the directed diffusion technique to achieve a global perspective
using only local nodes. In their algorithm, nodes are assigned levels,
with level 0 being the lowest level. When a node transmits a message, the
number of hops that the message travels is proportional to the node’s level.
A node can be promoted and demoted. Using this technique, higher level
nodes are able to communicate across clusters, while their lower level siblings
cannot. This effectively enables localized cluster computation while the
higher level nodes can coordinate their cluster’s local information to achieve
a global solution [22].

If an aggregation node is itself compromised, then all of the data being
delivered from the sensor network to the base station may be forged. To
detect this, Ye et al. describe a statistical en-route filtering mechanism [91].
It utilizes multiple MACs along the path from the aggregator to the base
station. Any packet that fails any of the MAC tests will be disregarded.
A more recent technique called TAG is proposed in [54]. In this case,
the authors propose an SQL like language that is used for generating queries
over the sensor network. The TAG approach is one of a general purpose
aggregation. That is, it has not been designed with an application specific
intent. It’s operation is fairly simple, the base station defines a query using
the SQL-like language designed for use in TAG. The sensors then route data

back to the base station according to a routing tree. At each point in the
tree, data is aggregated according to the routing tree and according to the
particular aggregation function that is defined in the initial query [54].
More recently Shrivastava et al. propose a summary structure that is
able to support fairly complex aggregate functions, such as median and
range queries [75]. It’s important to note that typical aggregate functions
are capable of performing min/max, sum, and average. The more complex
aggregates, such as finding the most frequent data values, are typically not
supported. They note that the added aggregate functions are not exact.
However, they prove strict guarantees on the approximation quality of the
queries [75].

Wagner analyzes the resilience of all aggregation techniques in [82], and
argues that current aggregation schemes were designed without security in
mind and that there are easy attacks against them. Wagner proposes a
mathematical framework for formally evaluating the security for aggregation,
allowing them to quantify the robustness of an aggregation operator
against malicious data. This seminal work opens the door to secure data
aggregation in sensor networks; however, the one-level homogeneous aggregation
model is too simple to represent real sensor network deployments.
Extending the model to a more realistic model, e.g., multi-level and heterogeneous,
is an interesting direction.

6.9.2 Secure Data Aggregation Techniques

As was shown above, the idea of information aggregation has been studied in
reasonable depth. The problem with the standard information aggregation
techniques, however, is that they assume that all nodes are trustworthy. Of
course, this is not the case and secure data aggregation techniques will be
necessary in many wireless sensor networks.
Przydatek et al. describe a secure information aggregation technique
(SIA) [68]. They note that sensor networks and data aggregation techniques
are vulnerable to a variety of attacks including denial of service attacks as
described in 5.2. However, [68] focus their efforts on defending specifically
against a type of attack called the stealthy attack. In a stealthy attack, the
attacker seeks to provide incorrect aggregation results to the user without
the user knowing that the results are incorrect. Therefore, the goal of [68] is
to ensure that if a user accepts an aggregate value as correct, then there is a
high probability that the value is close to the true aggregation value [68]. In
the event that the aggregate value has been tampered with, the user should

reject the incorrect results with high probability.
The approach that [68] provide is termed the aggregate-commit-prove
technique. As the name would suggest, the technique is composed of three
phases. In the first stage, aggregate, the aggregator collects data from the
sensors and computes the aggregation result according to a specific aggregate
function. Each sensor should share a key with the aggregator. This allows
the aggregator to verify that the sensor reading is authentic. However, it is
possible that a sensor has been compromised and possesses the key, or that
the sensor is simply malfunctioning. The aggregate phase does not prevent
such malfunctioning.

In the second phase, the commit phase, the aggregator is responsible
for committing to the collected data. This commitment ensures that the
aggregator actually uses the data collected from the sensors. One way to
perform this commitment is to use a Merkle hash-tree construction [56].
Using this technique the aggregator computes a hash of each input value and
the internal nodes are computed as the hash of their children concatenated.
The commitment is the root value. The hashing is used to ensure that the
aggregator cannot change any input values after having hashed them.
In the final phase, the aggregator is charged with proving the results
to the user. The aggregator first communicates the aggregation result and
the commitment. The aggregator then uses an interactive proof to prove
the correctness of the results. This generally requires two steps. In the
first, the user/home server checks to ensure that the committed data is a
good representation of the data values in the sensor network. In the second
step, the user/home server decides whether the aggregator is lying. This
can be done by checking whether or not the aggregation result is close to
the committed result [68]. The interactive proof differs depending on the
aggregation function that is being used.

Hu and Evans propose a secure aggregation technique that uses the
μTESLA protocol for security [33]. In this case, the nodes organize into
a tree based hierarchy where the internal nodes act as aggregators. Recall
that the μTESLA protocol achieves asymmetry through delayed discloser
of symmetric keys. Therefore, a child’s parent will be unable to immediately
verify the authenticity of the child’s data as the key used to generate
the MAC will not have been revealed. This technique, however, does not
guarantee that nodes and aggregators are providing correct values. To address
this problem, the base station is responsible for distributing temporary
keys to the network as well as the base station’s current μTESLA key, used
for validating MACs. Using the μTESLA key, nodes verify their children’s

MAC and are responsible for ensuring that the MACs are consistent.
To this end, we argue that secure aggregation techniques play an important
role in adopting wireless sensor networks, because of the large amount
of raw data and the necessity of the localized in-network processing, and
much more investigation is needed.
6.10 Defending Against Physical Attacks

Physical attacks, as we argued in the beginning of the chapter, pose a great
threat to wireless sensor networks, because of it’s unattended feature and
limited resources. Sensor nodes may be equipped with physical hardware to
enhance protection against various attacks. For example, to protect against
tampering with the sensors, one defense involves tamper-proofing the node’s
physical package [88]. [3, 4, 43] focus on building tamper-resistant hardware
in order to make the actual data and memory contents on the sensor chip
inaccessible to attack. Another way is to employ special software and hardware
outside the sensor to detect physical tampering.

As the price of the hardware itself gets cheaper, tamper-resistant hardware
may become more appropriate in a variety of sensor network deployments.
One possible approach to protect the sensors from physical attacks is
self-termination. The basic idea is the sensor kills itself, including destroy all
data and keys, when it senses a possible attack. This is particularly feasible
in the large scale wireless sensor network which has enough redundancy of
information, and the cost of a sensor is much cheaper than the lost of being
broken (attacked). The key of this approach is detecting the physical attack.
A simple solution is periodically conducting neighborhood checking in static
deployment. For mobile sensor networks, this is still an open problem.
In [3, 4, 43], the authors describe techniques for extracting protected
software and data from smartcard processors. This includes manual microprobing,
laser cutting, focused ion-beam manipulation, glitch attacks, and
power analysis, most of which are also possible physical attacks on the sensor.
Based on an analysis of these attacks, Andersen et al. give examples
of low-cost protection countermeasures that make such attacks considerably
more difficult, including [4]:
• Randomized Clock Signal Inserting random-time delays between
any observable reaction and critical operations that might be subject
to an attack.
• Randomized Multithreading Designing a multithread processor
architecture that schedules the processor by hardware between two or
more threads of execution randomly at a per-instruction level
• Robust Low-frequency Sensor Building an intrinsic self-test into
the detector. Any attempt to tamper with the sensor should result in
the malfunction of the entire processor.
• Destruction of Test Circuitry Destroying or disabling the special
test circuitry which is for the test engineers, closing the door to microprobing
attackers.
• Restricted Program Counter Avoid providing a program counter
that can run over the entire address space.
• Top-layer Sensor Meshes Introducing additional metal layers that
form a sensor mesh above the actual circuit and that do not carry any
critical signals to be effective annoyances to microprobing attackers.
For the deployment of components outside the sensor, various approaches
have been proposed to protect the sensor, and are summarized in [17]. Sastry
et al. [71] introduce the concept of secure location verification and propose
a secure localization scheme, the ECHO protocol, to make sure the location
claims are legitimate. In their work, the security rests on physical properties
of sound and RF signal propagation. An adversary cannot cheat and claim
a shorter distance by starting the ultra-sound response early, because it will
not have the nonce. Hu et al. [34] introduce directional antennas to defend
against wormhole attacks. In [85] the authors study the modeling and defense
of sensor networks against Search-based Physical Attacks. They define
a search-based physical attack model, where the attacker walks through the
sensor network using signal detecting equipment to locate active sensors,
and then destroys them. In a prior work, they have identified and modeled
blind physical attacks [84]. The defense algorithm is executed by individual
sensors in two phases: in the first phase, sensors detect the attacker
and send out attack notification messages to other sensors; in the second
phase, the recipient sensors of the notification message schedule their states
to switch. A mechanism named SWATT to verify whether the memory of a
sensor node has been changed [74] is proposed by Seshadri et al.
6.11 Trust Management
Trust is an old but important issue in any networked environment, whether
social networking or computer networking. Trust can solve some problems

beyond the power of the traditional cryptographic security. For example,
judging the quality of the sensor nodes and the quality of their services, and
providing the corresponding access control, e.g., does the data aggregator
perform the aggregation correctly? Does the forwarder send out the packet
in a timely fashion? These questions are important, but difficult, if not
impossible, to answer using existing security mechanisms. We argue that
trust management is the key to build trusted, dependable wireless sensor
network applications. The trust issue is emerging as sensor networks thrive.
However, it is not easy to build a good trust model within a sensor network
given the resource limits. Furthermore, in order to keep the sensor
nodes independent, we should not assume there is a trust among sensors in
advance.

According to the small world principle in the context of social networks
and peer-to-peer computing [60], one can employ a path-finder to find paths
from a source node to a designated target node efficiently. Based on this
observation, Zhu et al. [92] provide a practical approach to compute trust
in wireless networks by viewing individual mobile devices as a node of a
delegation graph G and mapping a delegation path from the source node
S to the target node T into an edge in the correspondent transitive closure
of the graph G, from which the trust value is computed. In this approach,
an undirected transitive signature scheme is used within the authenticated
transitive graphs.

In [90], a trust evaluation based security solution is proposed to provide
effective security decisions on data protection, secure routing, and other
network activities. Logical and computational trust analysis and evaluation
are deployed among network nodes. Each node’s evaluation of trust
on other nodes is based on serious study and inference from trust factors
such as experience statistics, data value, intrusion detection results, and
references to other nodes, as well as a node owner’s preference and policy.
Ren et al. describe a technique to establish sufficient trust relationships in
ad hoc networks with minimum local storage capacity requirements on the
mobile nodes [70]. The authors propose a probabilistic solution based on a
distributed trust model. A secret dealer is introduced only in the system
bootstrapping phase to complement the assumption in trust initialization.
With the help of the secret dealer, much shorter and more robust trust chains
are able to be constructed with high probability. A fully self-organized trust
establishment approach is then adopted to conform to the dynamic membership
changes. But the shortcoming of this approach for the common
sensor network is that it is not reasonable to introduce a dealer in a totally
decentralized ad hoc environment.

The approaches described above are proposed in the context of ad hoc
network. For the wireless sensor network, they can not be employed directly
because of the capacity of the sensor. Some researchers specifically
focus on the sensor networks that have been proposed recently. Ganeriwal
and Srivastava propose a reputation-based framework for high integrity
sensor networks [23]. Within this framework the authors employ a beta
reputation system for reputation representation, updates, and integration.
Tanachaiwiwat et al. [80] propose a mechanism of location-centric isolation
of misbehavior and trust routing in sensor networks. In their trust model,
the trustworthiness value is derived from the capacity of the cryptography,
availability and packet forwarding. If the trust value is below a specific trust
threshold, then this location is considered insecure and is avoided when forwarding
packets.

Liang and Shi focus on trust model developing and the analysis of rating
aggregation algorithms in the open untrusted environment [48, 49, 50].
Their findings and observations can be applied to wireless sensor networks
directly, although the work is performed in the context of peer-to-peer settings.
They propose a personalized trust model called PET in [50], which
supports the customization of trustworthiness from the view of individual
sensors. Regarding how to aggregate the ratings from referrals, they recently
analyze the effect of ratings on the trust inference in a comprehensive
way [48]. They find that the rating is not always helpful given the limitations
of other factors. In the open environment with high dynamics the rating
performance degrades and can produce negative effects. They observe that
the storage space for saving self-knowledge is a potential bottleneck to the
effect of ratings. Their recent simulation results show that it is better to
treat the ratings from different evaluators equally given the dynamics of the
open environment, and simply averaging ratings is appropriate considering
the simplicity of the algorithm design and the low cost in running the system.
They argue that the most important issue for building a trust model is
adjusting parameters according to environment changes. These suggestions
are quite useful for building trust models in the wireless sensor network given
their simplicity and cost savings.

7 Conclusions

In this chapter we have described the four main aspects of wireless sensor
network security: obstacles, requirements, attacks, and defenses. Within
each of those categories we have also sub-categorized the major topics including
routing, trust, denial of service, and so on. Our aim is to provide
both a general overview of the rather broad area of wireless sensor network
security, and give the main citations such that further review of the relevant
literature can be completed by the interested researcher.
As wireless sensor networks continue to grow and become more common,
we expect that further expectations of security will be required of these
wireless sensor network applications. In particular, the addition of publickey
cryptography and the addition of public-key based key management
described in 6.1.3 will likely make strong security a more realistic expectation
in the future. We also expect that the current and future work in privacy
and trust will make wireless sensor networks a more attractive option in a
variety of new arenas.

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6.4.2 Techniques for Securing the Routing Protocol

Deng, Han, and Mishra describe an intrusion tolerant routing protocol, INSENS,
that is designed to limit the scope of an intruder’s destruction and
route despite network intrusion without having to identify the intruder [15].
They note that an intruder need not be an actual intrusion on the sensor
network, but might simply be a node that is malfunctioning for no particularly
malicious reason. Identifying an actual intruder versus a malfunctioning
node can be extremely difficult, and for this reason Deng et al. make
no distinction between the two. The first technique they describe to mitigate
the damage done by a potential intruder is to simply employ the use
of redundancy. In this case, as described previously under denial of service,
multiple identical messages are routed between a source and destination. A
message is sent once along several distinct paths with the hope that at least
one will arrive at the destination. To discern which, if any, of the messages
arriving at the destination are authentic, an authentication scheme can be
employed to confirm the message’s integrity [15].

Deng et al. also make use of an assumed asymmetry between base stations
and wireless sensor nodes. They assume that the base stations are
somewhat less resource constrained than the individual sensor node. For
this reason, they suggest using the base station to compute routing tables
on behalf of the individual sensor nodes. This is done in three phases. In
the first phase, the base station broadcasts a request message to each neighbor
which is then propagated throughout the network. In the second phase,
the base station collects local connectivity information from each node. Finally,
the base station computes a series of forwarding tables for each node.
The forwarding tables will include the redundancy information used for the
redundant message transmission described above.
There are several possible attacks that can be made on the routing protocol
during each of the three stages described above. In the first phase, a node
might spoof the base station by sending a spurious request message [15]. A
malicious node might also include a fake path(s) when forwarding the re-

quest message to its neighbors. It may not even forward the request message
at all.

To counter this, Deng et al. use a scheme similar to μTESLA where
one-way key chains are used to identify a message originating from the base
station.

Tanachaiwiwat, et al. present a novel technique named TRANS (Trust
Routing for Location Aware Sensor Networks) [79]. The TRANS routing
protocol is designed for use in data centric networks. It also makes use
of a loose-time synchronization asymmetric cryptographic scheme to ensure
message confidentiality. In their implementation, μTESLA is used to ensure
message authentication and confidentiality. Using μTESLA, TRANS is able
to ensure that a message is sent along a path of trusted nodes while also using
location aware routing. The strategy is for the base station to broadcast
an encrypted message to all of its neighbors. Only those neighbors who are
trusted will possess the shared key necessary to decrypt the message. The
trusted neighbor(s) then adds its location (for the return trip), encrypts the
new message with its own shared key and forwards the message to its neighbor
closest to the destination. Once the message reaches the destination, the
recipient is able to authenticate the source (base station) using the MAC
that will correspond to the base station. To acknowledge or reply to the
message, the destination node can simply forward a return message along
the same trusted path from which the first message was received [79].
One particular challenge to secure routing in wireless sensor networks is
that it is very easy for a single node to disrupt the entire routing protocol
by simply disrupting the route discovery process. Papadimitratos and Haas
propose a secure route discovery protocol that guarantees, subject to several
conditions, that correct topological information will be obtained [62]. This
scenario is somewhat similar to the TRANS protocol mentioned above. The
security relies on the MAC (message authentication code) and an accumulation
of the node identities along the route traversed by a message. In so
doing, a source can discover the sensor network topology as each node along
the route from source to destination appends its identity to the message. In
order to ensure that the message has not been tampered with, a MAC is
constructed and can be verified both at the destination and the source (for
the return message from the destination).
A related problem is the concept of wormholes in a sensor network. A
wormhole attack is one in which a malicious node eavesdrops on a packet or
series of packets, tunnels them through the sensor network to another malicious
node, and then replays the packets. This can be done to misrepresent
the distance between the two colluding nodes. It can also be used to more
generally disrupt the routing protocol by misleading the neighbor discovery
process [40].

Often additional hardware, such as a directional antenna [34], is used
to defend against wormhole attacks. This, however, can be cost-prohibitive
when it comes to large-scale network deployment. Instead, Wang and Bhargava
use a visualization approach to identifying wormholes [83]. They first
compute a distance estimation between all neighbor sensors, including possible
existing wormholes. Using multi-dimensional scaling, they then compute
a virtual layout of the sensor network. A surface smoothing strategy is then
used to adjust for roundoff errors in the multi-dimensional scaling. Finally,
the shape of the resulting virtual network is analyzed. If a wormhole exists
within the network, the shape of the virtual network will bend and curve towards
the offending nodes. Using this strategy the nodes that participate in
the wormhole can be identified and removed from the network. If a network
does not contain a wormhole, the virtual network will appear flat [83].

6.4.3 Defending Against the Sybil Attack

To defend against the Sybil attack described previously in Section 5.3, the
network needs some mechanism to validate that a particular identify is the
only identity being held by a given physical node [59]. Newsome et al. describe
two methods to validate identities, direct validation and indirect validation.
In direct validation a trusted node directly tests whether the joining
identity is valid. In indirect validation, another trusted node is allowed to
vouch for (or against) the validity of a joining node [59]. Newsome et al.
primarily describe direct validation techniques, including a radio resource
test. In the radio test, a node assigns each of its neighbors a different channel
on which to communicate. The node then randomly chooses a channel
and listens. If the node detects a transmission on the channel it is assumed
that the node transmitting on the channel is a physical node. Similarly, if
the node does not detect a transmission on the specified channel, the node
assumes that the identity assigned to the channel is not a physical identity.
Another technique to defend against the Sybil attack is to use random
key pre-distribution techniques. The idea behind this technique is that with
a limited number of keys on a keyring, a node that randomly generates
identities will not possess enough keys to take on multiple identities and
thus will be unable to exchange messages on the network due to the fact
that the invalid identity will be unable to encrypt or decrypt messages.

6.5 Detecting Node Replication Attacks

In [63], Parno, et al. describe two algorithms: randomized multicast, and
line-selected multicast. Randomized multicast is an evolution of a node
broadcasting strategy. In the simple node broadcasting strategy each sensor
propagates an authenticated broadcast message throughout the entire sensor
network. Any node that receives a conflicting or duplicated claim revokes the
conflicting nodes [63]. This strategy will work, but the communication cost is
far too expensive. In order to reduce the communication cost, a deterministic
multicast could be employed where nodes would share their locations with
a set of witness nodes. In this case, witnesses are computed based on a
node’s ID. In the event that a node has been replicated on the network, two
conflicting locations will be forwarded to the same witness who can then
revoke the offending nodes [63]. But since a witness is based on a node’s
ID, it can easily be computed by an attacker who can then compromise the
witness nodes. Thus, securely utilizing a deterministic multicast strategy
would require too many witnesses and the communication cost would be
too high.

Randomized multicast improves upon the insecurity of deterministic
multicast by randomly choosing the witnesses. In the event that a node
is replicated two sets of witness nodes are chosen. Assuming a network of
size n, if each node derives
p
n witnesses then the birthday paradox suggests
that there will likely be at least one collision [63]. In the event that a collision
is detected, the offending nodes can easily be revoked by propagating
a revocation throughout the network. Unfortunately, the communication
cost of the randomized multicast algorithm is still O(n2) - too high for large
networks.

The line-selected multicast algorithm seeks to further reduce the communication
costs of the randomized multicast algorithm. It is based upon
rumor routing described in [8]. The idea is that a location claim travelling
from source s to destination d will also travel through several intermediate
nodes. If each of these nodes records the location claim, then the path of the
location claim through the network can be thought of as a line segment [63].
In this case the destination of the location claims is one of the randomly chosen
witnesses described in the multicast algorithm. As the location claim
routes through the network towards a witness node, the intermediate sensors
check the claim. If the claim results in an intersection of a line segment
then the nodes originating the conflicting claims are revoked. The line selected
multicast algorithm reduces the communication cost to O(n
p
n) as
long as each line segment is of length O(
p
n) nodes. The storage cost of the
line-selected multicast algorithm is O(
p
n) [63].

6.6 Combating Traffic Analysis Attacks

Strategies to combat the traffic analysis attacks previously described are
possible. Deng et al. propose using a random walk forwarding technique
that occasionally forwards a packet to a node other than the sensor’s parent
node [16]. This would make it difficult to discern a clear path from the
senor to the base station and would help to mitigate the rate monitoring
attack, but would still be vulnerable to the time correlation attack. To
defend against the time correlation attack, Deng et al. suggest a fractal
propagation strategy [16]. In this technique a node will (with a certain
probability) generate a fake packet when its neighbor is forwarding a packet
to the base station. The fake packet is sent randomly to another neighbor
who may also generate a fake packet. These packets essentially use a timeto-
live (TTL) to decide when forwarding should stop. This effectively hides
the base station from time correlation attacks. Since traffic analysis is closely
related to privacy violation, we discus traffic analysis to the next subsection.

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6.2 Defending Against DoS Attacks

In Table 2 the most common layers of a typical wireless sensor network are
summarized along with their attacks and defenses. Since denial of service
attacks are so common (see Section 5), effective defenses must be available
to combat them. One strategy in defending against the classic jamming
attack is to identify the jammed part of the sensor network and effectively

route around the unavailable portion. Wood and Stankovic [88] describe
a two phase approach where the nodes along the perimeter of the jammed
region report their status to their neighbors who then collaboratively define
the jammed region and simply route around it.
To handle jamming at the MAC layer, nodes might utilize a MAC admission
control that is rate limiting. This would allow the network to ignore
those requests designed to exhaust the power reserves of a node. This, however,
is not fool-proof as the network must be able to handle any legitimately
large traffic volumes.

Overcoming rogue sensors that intentionally misroute messages can be
done at the cost of redundancy. In this case, a sending node can send the
message along multiple paths in an effort to increase the likelihood that the
message will ultimately arrive at its destination. This has the advantage of
effectively dealing with nodes that may not be malicious, but rather may
have simply failed as it does not rely on a single node to route its messages.
To overcome the transport layer flooding denial of service attack Aura,
Nikander and Leiwo suggest using the client puzzles posed by Juels and
Brainard [5] in an effort to discern a node’s commitment to making the
connection by utilizing some of their own resources. Aura et al. advocate
that a server should force a client to commit its own resources first. Further,
they suggest that a server should always force a client to commit more
resources up front than the server. This strategy would likely be effective as
long as the client has computational resources comparable to those of the
server.

6.3 Secure Broadcasting and Multicasting
The research community of wireless sensor networks has progressively reached
a consensus that the major communication pattern of wireless sensor networks
is broadcasting and multicasting, e.g., 1-to-N, N-to-1, and M-to-N,
instead of the traditional point-to-point communication on the Internet.
Next we examine the current state of research in secure broadcasting and
multicasting. As we will see, in wireless sensor networks, a great deal of
the security derives from ensuring that only members of the broadcast or
multicast group possess the required keys in order to decrypt the broadcast
or multicast messages. Because of this, most of the work presented
in 6.1 is still applicable. Here, however, we will address those schemes that
have been specifically designed to support broadcasting and multicasting in
wireless sensor networks.

6.3.1 Traditional Broadcasting and Multicasting

Traditionally, multicasting and broadcasting techniques have been used to
reduce the communication and management overhead of sending a single
message to multiple receivers. In order to ensure that only certain users receive
the multicast or broadcast, encryption techniques must be employed.
In both a wired and wireless network this is done using cryptography. The
problem then is one of key management. To handle this, several key management
schemes have been devised: centralized group key management protocols,
decentralized management protocols, and distributed management
protocols [69].

In the case of the centralized group key management protocols, a central
authority is used to maintain the group. Decentralized management
protocols, however, divide the task of group management amongst multiple
nodes. Each node that is responsible for part of the group management is
responsible for a certain subset of the nodes in the network. In the last
case, distributed key management protocols, there is no single key management
authority. Therefore, the entire group of nodes are responsible for key
management [69].
In order to efficiently distribute keys, one well known technique is to
use a logical key tree. Such a technique falls into the centralized group key
management protocols. This technique has been extended to wireless sensor
networks in [66, 46, 45]. While centralized solutions are often not ideal, in
the case of wireless sensor networks a centralized solution offers some utility.
Such a technique allows a more powerful base station to offload some of the
computations from the less powerful sensor nodes.

6.3.2 Secure Multicasting

Di Pietro et al. describe a directed diffusion based multicast technique for
use in wireless sensor networks that also takes advantage of a logical key
hierarchy [66]. In a standard logical key hierarchy a central key distribution
center is responsible for disbursing the keys throughout the network. The
key distribution center, therefore, is the root of the key hierarchy while
individual nodes make up the leaves. The internal nodes of the key hierarchy
contain keys that are used in the re-keying process [66].
Directed diffusion is a data-centric, energy efficient dissemination technique
that has been designed for use in wireless sensor networks [38]. In
directed diffusion, a query is transformed into an interest (due to the data-

centric nature of the network). The interest is then diffused throughout
the network and the network begins collecting data based on that interest.
The dissemination technique also sets up certain gradients designed to draw
events toward the interest. Data collected as a result of the interest can
then be sent back along the reverse path of the interest propagation [38].
Using the above mentioned directed diffusion technique, Di Pietro et al.
enhance the logical key hierarchy to create a directed diffusion based logical
key hierarchy. The logical key hierarchy technique provides mechanisms
for nodes joining and leaving groups where the key hierarchy is used to
effectively re-key all nodes within the leaving node’s hierarchy [66]. The
directed diffusion is also used in node joining and leaving. When a node
declares an intent to join, for example, a join “interest” is generated which
travels down the gradient of “interest about interest to join” [66]. When a
node joins, a key set is generated for the new node based on keys within the
key hierarchy.

Kaya et al. discuss the problem of multicast group management in [42].
In this case, nodes are grouped based on locality and attach to a security tree.
However, they assume that nodes within the mobile network are somewhat
more powerful than a traditional sensor in a wireless sensor network.
6.3.3 Secure Broadcasting

Lazos and Poovendran describe a tree based key distribution scheme that
is similar to [66]. They suggest a routing-aware based tree where the leaf
nodes are assigned keys based on all relay nodes above them. They argue
that their technique, which takes advantage of routing information, is more
energy efficient than routing schemes that arbitrarily arrange nodes into
the routing tree. They propose a greedy routing-aware key distribution
algorithm [45].

In [46], Lazos and Poovendran use a similar technique to [45], but instead
use geographic location information (e.g., GPS) rather than routing
information. In this case, however, nodes (with the help of the geographic
location system) are grouped into clusters with the observation that nodes
within a cluster will be able to reach one another with a single broadcast.
Using the cluster information, a key hierarchy is constructed as in [45].

6.4 Defending Against Attacks on Routing Protocols
Routing in wireless sensor networks has, to some extent, been reasonably
well studied. However, most current research has focused primarily on providing
the most energy efficient routing. There is a great need for both secure
and energy efficient routing protocols in wireless sensor networks as attacks
such as the sinkhole, wormhole and Sybil attacks demonstrate [35, 40, 59].
As wireless sensor networks continue to grow in size and utility, routing security
must not be an after-thought, but rather they must be included as
part of the overall sensor network design. This section describes the current
state of routing security as it applies to wireless sensor networks.

6.4.1 Background

Because wireless sensors are designed to be widely distributed power and
computationally constrained networks, efficient routing protocols must be
used in order to maximize the battery life of each node. There are a variety
of routing protocols in use in wireless sensor networks, so it is not possible
to provide a single security protocol that will be able to secure each type
of routing protocol. Before introducing several techniques used to provide
secure routing in wireless sensor networks, we will begin with a general
overview of several routing protocols that are currently in use. An excellent
discussion on many of the attacks on routing protocols is also discussed
in [40].

In general, packet routing algorithms are used to exchange messages with
sensor nodes that are outside of a particular radio range. This is different
than to sensors that are within radio range where packets can be transmitted
using a single hop. In such single hop networks security is still a concern, but
is more accurately addressed through secure broadcasting and multicasting.
The first packet routing algorithm is based on node identifiers similar
to traditional routing. In this case, each sensor is identified by an address
and routing to/from the sensor is based on the address. This is generally
considered inefficient in sensor networks, where nodes are expected to be
addressed by their location, rather than their identifier.
As a consequence of the distaste of routing based on node identifiers,
geographic routing protocols have been introduced [41, 7]. One common
routing protocol, GPSR [41] allows nodes to send a packet to a region,
rather than a particular node. Such a routing protocol lends itself nicely to
the concept of data-centric networks. A data-centric network is one in which

data are stored by name in the sensor network. Data with the same name
are stored at the same node. In fact, data need not be stored anywhere
near the sensor responsible for generating the data. When searching the
network, searches are therefore based on the data’s general name, rather
than the identity responsible for holding the data. Security specific to this
type of network is discussed in [79].

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6 Defensive Measures

Now we are in a position to describe the measures for satisfying security requirements,
and protecting the sensor network from attacks. We start with
key establishment in wireless sensor networks, which lays the foundation
for the security in a wireless sensor network, followed by defending against
DoS attacks, secure broadcasting and multicasting, defending against attacks
on routing protocols, combating traffic analysis attacks, defending against
attacks on sensor privacy, intrusion detection, secure data aggregation, defending
against physical attacks, and trust management.

6.1 Key Establishment

One security aspect that receives a great deal of attention in wireless sensor
networks is the area of key management. Wireless sensor networks are
unique (among other embedded wireless networks) in this aspect due to their
size, mobility and computational/power constraints. Indeed, researchers envision
wireless sensor networks to be orders of magnitude larger than their
traditional embedded counterparts. This, coupled with the operational constraints
described previously, makes secure key management an absolute
necessity in most wireless sensor network designs. Because encryption and
key management/establishment are so crucial to the defense of a wireless
sensor network, with nearly all aspects of wireless sensor network defenses
relying on solid encryption, we first begin with an overview of the unique key
and encryption issues surrounding wireless sensor networks before discussing
more specific sensor network defenses.

6.1.1 Background

Key management issues in wireless networks are not unique to wireless sensor
networks. Indeed, key establishment and management issues have been
studied in depth outside of the wireless networking arena. Traditionally,
key establishment is done using one of many public-key protocols. One of

the more common is the Diffie-Hellman public key protocol, but there are
many others.
Most of the traditional techniques, however, are unsuitable in low power
devices such as wireless sensor networks. This is due largely to the fact that
typical key exchange techniques use asymmetric cryptography, also called
public key cryptography. In this case, it is necessary to maintain two mathematically
related keys, one of which is made public while the other is kept
private. This allows data to be encrypted with the public key and decrypted
only with the private key. The problem with asymmetric cryptography, in a
wireless sensor network, is that it is typically too computationally intensive
for the individual nodes in a sensor network. This is true in the general case,
however, [25, 29, 55, 87] show that it is feasible with the right selection of
algorithms.
Symmetric cryptography is therefore the typical choice for applications
that cannot afford the computational complexity of asymmetric cryptography.
Symmetric schemes utilize a single shared key known only between
the two communicating hosts. This shared key is used for both encrypting
and decrypting data. The traditional example of symmetric cryptography
is DES (Data Encryption Standard). The use of DES, however, is quite
limited due to the fact that it can be broken relatively easily. In light of
the shortcomings of DES, other symmetric cryptography systems have been
proposed including 3DES (Triple DES), RC5, AES, and so on [73].
An analysis of the various ciphers is presented in [44] with a summary
of their results shown in Table 1. The table shows two different rankings
- one by key setup and the other by encryption mode. In both rankings,
algorithms are optimized for both speed and size, and are ranked by speed,
code size and data size within both the speed and size categories (see Table
1). From the key setup table, we can see that MISTY1 seems to generally
perform the best with top finishes in data memory and speed in both size
optimized and speed optimized categories. When comparing the algorithms
by encryption/decryption, the winner seems less clear. Again, MISTY1 performs
well, finishing within the top three in each category. RC5-32, on the
other hand, has an apparent advantage in both data memory and code memory
at the expense of speed. By examining the number of CPU cycles, [44]
concludes that the most energy efficient cipher listed in Table 1 is Rijndael.
Their reasoning is that fewer CPU cycles translates directly into less energy
used.
One major shortcoming of symmetric cryptography is the key exchange
problem. Simply put, the key exchange problem derives from the fact that
16
By key setup:
Rank Size Optimized Speed Optimized
Code mem. Data mem. Speed Code mem. Data mem. Speed
1 RC5-32 MISTY1 MISTY1 RC6-32 MISTY1 MISTY1
2 KASUMI Rijndael Rijndael KASUMI Rijndael Rinjdael
3 RC6-32 KASUMI KASUMI RC5-32 KASUMI KASUMI
4 MISTY1 RC6-32 Camellia MISTY1 RC6-32 Camellia
5 Rijndael RC5-32 RC5-32 Rijndael Camellia RC5-32
6 Camellia Camellia RC6-32 Camellia RC5-32 RC6-32
By encryption (CBC/CFB/OFB/CTR)
Rank Size Optimized Speed Optimized
Code mem. Data mem. Speed Code mem. Data mem. Speed
1 RC5-32 RC5-32 Rijndael RC6-32 RC5-32 Rijndael
2 RC6-32 MISTY1 MISTY1 RC5-32 MISTY1 Camellia
3 MISTY1 KASUMI KASUMI MISTY1 KASUMI MISTY1
4 KASUMI RC6-32 Camellia KASUMI RC6-32 RC5-32
5 Rijndael Rijndael RC6-32 Rijndael Rijndael KASUMI
6 Camellia Camellia RC5-32 Camellia Camellia RC6-32
Table 1: A summary of cipher performance from [44].
two communicating hosts must somehow know the shared key before they
can communicate securely. So the problem that arises is how to ensure
that the shared key is indeed shared between the two hosts who wish to
communicate and no other rogue hosts who may wish to eavesdrop. How
to distribute a shared key securely to communicating hosts is a non-trivial
problem since pre-distributing the keys is not always feasible.
6.1.2 Key Establishment and Associated Protocols
Random key pre-distribution schemes have several variants [13, 21, 37, 53].
Eschenauer and Gligor propose a key pre-distribution scheme [21] that relies
on probabilistic key sharing among nodes within the sensor network. Their
system works by distributing a key ring to each participating node in the
sensor network before deployment. Each key ring should consist of a number
randomly chosen keys from a much larger pool of keys generated offline. An
enhancement to this technique utilizing multiple keys is described in [13].
Further enhancements are proposed in [19, 53] with additional analysis and
enhancements provided by [37].
Using this technique, it is not necessary that each pair of nodes share
a key. However, any two nodes that do share a key may use the shared
key to establish a direct link to one another. Eschenauer and Gligor show
that, while not perfect, it is probabilistically likely that large sensor networks
will enjoy shared-key connectivity. Further, they demonstrate that
17
such a technique can be extended to key revocation, re-keying, and the
addition/deletion of nodes.
The LEAP protocol described by Zhu et al. [93] takes an approach that
utilizes multiple keying mechanisms. Their observation is that no single
security requirement accurately suites all types of communication in a wireless
sensor network. Therefore, four different keys are used depending on
whom the sensor node is communicating with. Sensors are preloaded with
an initial key from which further keys can be established. As a security
precaution, the initial key can be deleted after its use in order to ensure
that a compromised sensor cannot add additional compromised nodes to
the network.
In PIKE [12], Chan and Perrig describe a mechanism for establishing a
key between two sensor nodes that is based on the common trust of a third
node somewhere within the sensor network. The nodes and their shared
keys are spread over the network such that for any two nodes A and B,
there is a node C that shares a key with both A and B. Therefore, the key
establishment protocol between A and B can be securely routed through C.
Huang et al. [36] propose a hybrid key establishment scheme that makes
use of the difference in computational and energy constraints between a
sensor node and the base station. They posit that an individual sensor
node possesses far less computational power and energy than a base station.
In light of this, they propose placing the major cryptographic burden on
the base station where the resources tend to be greater. On the sensor side,
symmetric-key operations are used in place of their asymmetric alternatives.
The sensor and the base station authenticate based on elliptic curve cryptography.
Elliptic curve cryptography is often used in sensors due to the
fact that relatively small key lengths are required to achieve a given level of
security.
Huang et al. also use certificates to establish the legitimacy of a public
key. The certificates are based on an elliptic curve implicit certificate
scheme [36]. Such certificates are useful to ensure both that the key belongs
to a device and that the device is a legitimate member of the sensor network.
Each node obtains a certificate before joining the network using an
out-of-band interface.
6.1.3 Public Key Cryptography
Two of the major techniques used to implement public-key cryptosystems
are RSA and elliptic curve cryptography (ECC) [73]. Traditionally, these

have been thought to be far too heavyweight for use in wireless sensor networks.
Recently, however, several groups have successfully implemented
public-key cryptography (to varying degrees) in wireless sensor networks.
In [29] Gura et al. report that both RSA and elliptic curve cryptography
are possible using 8-bit CPUs with ECC, demonstrating a performance advantage
over RSA. Another advantage is that ECC’s 160 bit keys result in
shorter messages during transmission compared the 1024 bit RSA keys. In
particular Gura et al. demonstrate that the point multiplication operations
in ECC are an order of magnitude faster than private-key operations within
RSA, and are comparable (though somewhat slower) to the RSA public-key
operation [29].
In [87], Watro et al. show that portions of the RSA cryptosystem can be
successfully applied to actual wireless sensors, specifically the UC Berkeley
MICA2 motes [32]. In particular, they implemented the public operations
on the sensors themselves while offloading the private operations to devices
better suited for the larger computational tasks. In this case, a laptop was
used.
The TinyPK system described by [87] is designed specifically to allow authentication
and key agreement between resource constrained sensors. The
agreed upon keys may then be used in conjunction with the existing cryptosystem,
TinySec [39]. To do this, they implement the Diffie-Hellman key
exchange algorithm and perform the public-key operations on the Berkeley
motes.
The Diffie-Hellman key exchange algorithm used in [55] is depicted in
Figure 1. In this case, a point G is selected from an elliptic curve E, both
of which are public. A random integer KA is selected, which will act as
the private key. The public key (TA in the case of Alice from Figure 1) is
then TA = KA ¤ G. Bob performs a similar set of operations to compute
TB = KB ¤ G. Alice and Bob can now easily compute the shared-secret
using their own private keys and the public keys that have been exchanged.
In this case, Alice computes KA ¤ TB = KA ¤ KB ¤ G while Bob computes
KB ¤ TA = KB ¤ KA ¤ G. Because KA ¤ TB = KB ¤ TA, Alice and Bob now
share a secret key.
As stated above, the elliptic curve cryptography shows promise over that
of RSA due to its efficiency compared to the private-key operations of RSA.
Further, using ECC, the key length required to securely transmit Tiny-
Sec keys can be as small as 163 bits rather than the 1024 bits required in
RSA. In [55], Malan et al. demonstrate a working implementation of Diffie-
Hellman based on the Elliptic Curve Discrete Logarithm Problem (Figure 1).
19
compute K B * T A
A
T B = K B * G
A
compute K A * T B
A T = K * G Alice chooses random K Bob chooses random K B
agree on E, G
Agree on KA * K B * G
Elliptic Curve Diffie−Hellman
Figure 1: The Diffie-Hellman Elliptic Curve Key Exchange Algorithm [55].
Network Layer Attacks Defenses
Physical Jamming Spread-spectrum,
priority messages,
lower duty cycle,
region mapping,
mode change
Tampering Tamper-proof, hiding
Link Collision Error correcting code
Exhaustion Rate limitation
Unfairness Small frames
Neglect and greed Redundancy, probing
Network Homing Encryption
and routing Misdirection Egress filtering,
authorization monitoring
Black holes Authorization,
monitoring, redundancy
Transport Flooding Client Puzzles
Desynchronization Authentication
Table 2: Sensor network layers and DoS attacks/defenses [88].
And while key generation is by no means fast or inexpensive (34.161 seconds
to generate a public/private-key pair and 34.173 seconds to generate a
shared secret with Diffie-Hellman [55]), it is sufficient for infrequent use in
generating keys in the TinySec protocols.

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5 Attacks

Sensor networks are particularly vulnerable to several key types of attacks.
Attacks can be performed in a variety of ways, most notably as denial of
service attacks, but also through traffic analysis, privacy violation, physical
attacks, and so on. Denial of service attacks on wireless sensor networks can
range from simply jamming the sensor’s communication channel to more
sophisticated attacks designed to violate the 802.11 MAC protocol [64] or
any other layer of the wireless sensor network.
Due to the potential asymmetry in power and computational constraints,
guarding against a well orchestrated denial of service attack on a wireless
sensor network can be nearly impossible. A more powerful node can easily
jam a sensor node and effectively prevent the sensor network from performing
its intended duty.
We note that attacks on wireless sensor networks are not limited to
simply denial of service attacks, but rather encompass a variety of techniques
including node takeovers, attacks on the routing protocols, and attacks on
a node’s physical security. In this section, we first address some common
denial of service attacks and then describe additional attacking, including
those on the routing protocols as well as an identity based attack known as
the Sybil attack.

5.1 Background

Wood and Stankovic define one kind of denial of service attack as “any
event that diminishes or eliminates a network’s capacity to perform its expected
function” [88]. Certainly, denial of service attacks are not a new
phenomenon. In fact, there are several standard techniques used in traditional
computing to cope with some of the more common denial of service

techniques, although this is still an open problem to the network security
community. Unfortunately, wireless sensor networks cannot afford the computational
overhead necessary in implementing many of the typical defensive
strategies.
What makes the prospect of denial of service attacks even more alarming
is the projected use of sensor networks in highly critical and sensitive applications.
For example, a sensor network designed to alert building occupants
in the event of a fire could be highly susceptible to a denial of service attack.
Even worse, such an attack could result in the deaths of building occupants
due to the non-operational fire detection network.
Other possible uses for wireless sensors include the monitoring of traffic
flows which may include the control of traffic lights, and so forth. A denial
of service attack on such a sensor network could prove very costly, especially
on major roads.
For this reason, researchers have spent a great deal of time both identifying
the various types of denial of service attacks and devising strategies
to subvert such attacks. We describe now some of the major types of denial
of service attacks.

5.2 Types of Denial of Service attacks

A standard attack on wireless sensor networks is simply to jam a node
or set of nodes. Jamming, in this case, is simply the transmission of a
radio signal that interferes with the radio frequencies being used by the
sensor network [88]. The jamming of a network can come in two forms:
constant jamming, and intermittent jamming. Constant jamming involves
the complete jamming of the entire network. No messages are able to be
sent or received. If the jamming is only intermittent, then nodes are able to
exchange messages periodically, but not consistently. This too can have a
detrimental impact on the sensor network as the messages being exchanged
between nodes may be time sensitive [88].
Attacks can also be made on the link layer itself. One possibility is that
an attacker may simply intentionally violate the communication protocol,
e.g., ZigBee [94] or IEEE 801.11b (Wi-Fi) protocol, and continually transmit
messages in an attempt to generate collisions. Such collisions would require
the retransmission of any packet affected by the collision. Using this technique
it would be possible for an attacker to simply deplete a sensor node’s
power supply by forcing too many retransmissions.
At the routing layer, a node may take advantage of a multihop network

by simply refusing to route messages. This could be done intermittently or
constantly with the net result being that any neighbor who routes through
the malicious node will be unable to exchange messages with, at least, part
of the network. Extensions to this technique including intentionally routing
messages to incorrect nodes (misdirection) [88].
The transport layer is also susceptible to attack, as in the case of flooding.
Flooding can be as simple as sending many connection requests to a
susceptible node. In this case, resources must be allocated to handle the
connection request. Eventually a node’s resources will be exhausted, thus
rendering the node useless.

5.3 The Sybil attack

Newsome et al. describe the Sybil attack as it relates to wireless sensor
networks [59]. Simply put, the Sybil attack is defined as a “malicious device
illegitimately taking on multiple identities”[59]. It was originally described
as an attack able to defeat the redundancy mechanisms of distributed
data storage systems in peer-to-peer networks [18]. In addition to defeating
distributed data storage systems, the Sybil attack is also effective against
routing algorithms, data aggregation, voting, fair resource allocation and
foiling misbehavior detection. Regardless of the target (voting, routing, aggregation),
the Sybil algorithm functions similarly. All of the techniques
involve utilizing multiple identities. For instance, in a sensor network voting
scheme, the Sybil attack might utilize multiple identities to generate additional
“votes.” Similarly, to attack the routing protocol, the Sybil attack
would rely on a malicious node taking on the identity of multiple nodes, and
thus routing multiple paths through a single malicious node.

5.4 Traffic Analysis Attacks

Wireless sensor networks are typically composed of many low-power sensors
communicating with a few relatively robust and powerful base stations. It
is not unusual, therefore, for data to be gathered by the individual nodes
where it is ultimately routed to the base station. Often, for an adversary
to effectively render the network useless, the attacker can simply disable
the base station. To make matters worse, Deng et al. demonstrate two attacks
that can identify the base station in a network (with high probability)
without even understanding the contents of the packets (if the packets are
themselves encrypted) [16].

A rate monitoring attack simply makes use of the idea that nodes closest
to the base station tend to forward more packets than those farther away
from the base station. An attacker need only monitor which nodes are
sending packets and follow those nodes that are sending the most packets. In
a time correlation attack, an adversary simply generates events and monitors
to whom a node sends its packets. To generate an event, the adversary could
simply generate a physical event that would be monitored by the sensor(s)
in the area (turning on a light, for instance) [16].

5.5 Node Replication Attacks

Conceptually, a node replication attack is quite simple: an attacker seeks to
add a node to an existing sensor network by copying (replicating) the node
ID of an existing sensor node [63]. A node replicated in this fashion can
severely disrupt a sensor network’s performance: packets can be corrupted
or even misrouted. This can result in a disconnected network, false sensor
readings, etc. If an attacker can gain physical access to the entire network
he can copy cryptographic keys to the replicated sensor and can also insert
the replicated node into strategic points in the network [63]. By inserting
the replicated nodes at specific network points, the attacker could easily
manipulate a specific segment of the network, perhaps by disconnecting it
altogether.

5.6 Attacks Against Privacy

Sensor network technology promises a vast increase in automatic data collection
capabilities through efficient deployment of tiny sensor devices. While
these technologies offer great benefits to users, they also exhibit significant
potential for abuse. Particularly relevant concerns are privacy problems,
since sensor networks provide increased data collection capabilities [28]. Adversaries
can use even seemingly innocuous data to derive sensitive information
if they know how to correlate multiple sensor inputs. For example, in
the famous “panda-hunter problem” [61], the hunter can imply the position
of pandas by monitoring the traffic.
The main privacy problem, however, is not that sensor networks enable
the collection of information. In fact, much information from sensor networks
could probably be collected through direct site surveillance. Rather,
sensor networks aggravate the privacy problem because they make large
volumes of information easily available through remote access. Hence, ad-

versaries need not be physically present to maintain surveillance. They can
gather information in a low-risk, anonymous manner. Remote access also
allows a single adversary to monitor multiple sites simultaneously [11]. Some
of the more common attacks [28, 11] against sensor privacy are:
• Monitor and Eavesdropping This is the most obvious attack to
privacy. By listening to the data, the adversary could easily discover
the communication contents. When the traffic conveys the control
information about the sensor network configuration, which contains
potentially more detailed information than accessible through the location
server, the eavesdropping can act effectively against the privacy
protection.
• Traffic Analysis Traffic analysis typically combines with monitoring
and eavesdropping. An increase in the number of transmitted packets
between certain nodes could signal that a specific sensor has registered
activity. Through the analysis on the traffic, some sensors with special
roles or activities can be effectively identified.
• Camouflage Adversaries can insert their node or compromise the
nodes to hide in the sensor network. After that these nodes can masquerade
as a normal node to attract the packets, then misroute the
packets, e.g. forward the packets to the nodes conducting the privacy
analysis.
It is worth noting that, as pointed out in [64], the current understanding
of privacy in wireless sensor networks is immature, and more research is
needed.

5.7 Physical Attacks

Sensor networks typically operate in hostile outdoor environments. In such
environments, the small form factor of the sensors, coupled with the unattended
and distributed nature of their deployment make them highly susceptible
to physical attacks, i.e., threats due to physical node destructions [86].
Unlike many other attacks mentioned above, physical attacks destroy sensors
permanently, so the losses are irreversible. For instance, attackers can
extract cryptographic secrets, tamper with the associated circuitry, modify
programming in the sensors, or replace them with malicious sensors under
the control of the attacker [85]. Recent work has shown that standard sensor
nodes, such as the MICA2 motes, can be compromised in less than one

minute [30]. While these results are not surprising given that the MICA2
lacks tamper resistant hardware protection, they provide a cautionary note
about the speed of a well-trained attacker. If an adversary compromises a
sensor node, then the code inside the physical node may be modified.

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4 Security Requirements

A sensor network is a special type of network. It shares some commonalities
with a typical computer network, but also poses unique requirements
of its own as discussed in Section 3. Therefore, we can think of the requirements
of a wireless sensor network as encompassing both the typical
network requirements and the unique requirements suited solely to wireless
sensor networks.

4.1 Data Confidentiality

Data confidentiality is the most important issue in network security. Every
network with any security focus will typically address this problem first. In
sensor networks, the confidentiality relates to the following [10, 65]:
• A sensor network should not leak sensor readings to its neighbors.
Especially in a military application, the data stored in the sensor node
may be highly sensitive.
• In many applications nodes communicate highly sensitive data, e.g.,
key distribution, therefore it is extremely important to build a secure
channel in a wireless sensor network.
• Public sensor information, such as sensor identities and public keys,
should also be encrypted to some extent to protect against traffic analysis
attacks.
The standard approach for keeping sensitive data secret is to encrypt the
data with a secret key that only intended receivers possess, thus achieving
confidentiality.

4.2 Data Integrity

With the implementation of confidentiality, an adversary may be unable
to steal information. However, this doesn’t mean the data is safe. The
adversary can change the data, so as to send the sensor network into disarray.
For example, a malicious node may add some fragments or manipulate the
data within a packet. This new packet can then be sent to the original
receiver. Data loss or damage can even occur without the presence of a
malicious node due to the harsh communication environment. Thus, data
integrity ensures that any received data has not been altered in transit.
4.3 Data Freshness
Even if confidentiality and data integrity are assured, we also need to ensure
the freshness of each message. Informally, data freshness suggests that the
data is recent, and it ensures that no old messages have been replayed. This
requirement is especially important when there are shared-key strategies
employed in the design. Typically shared keys need to be changed over
time. However, it takes time for new shared keys to be propagated to the

entire network. In this case, it is easy for the adversary to use a replay
attack. Also, it is easy to disrupt the normal work of the sensor, if the
sensor is unaware of the new key change time. To solve this problem a
nonce, or another time-related counter, can be added into the packet to
ensure data freshness.

4.4 Availability

Adjusting the traditional encryption algorithms to fit within the wireless
sensor network is not free, and will introduce some extra costs. Some approaches
choose to modify the code to reuse as much code as possible. Some
approaches try to make use of additional communication to achieve the same
goal. What’s more, some approaches force strict limitations on the data access,
or propose an unsuitable scheme (such as a central point scheme) in
order to simplify the algorithm. But all these approaches weaken the availability
of a sensor and sensor network for the following reasons:
• Additional computation consumes additional energy. If no more energy
exists, the data will no longer be available.
• Additional communication also consumes more energy. What’s more,
as communication increases so too does the chance of incurring a communication
conflict.
• A single point failure will be introduced if using the central point
scheme. This greatly threatens the availability of the network.
The requirement of security not only affects the operation of the network,
but also is highly important in maintaining the availability of the whole
network.
4.5 Self-Organization
A wireless sensor network is a typically an ad hoc network, which requires every
sensor node be independent and flexible enough to be self-organizing and
self-healing according to different situations. There is no fixed infrastructure
available for the purpose of network management in a sensor network. This
inherent feature brings a great challenge to wireless sensor network security
as well. For example, the dynamics of the whole network inhibits the
idea of pre-installation of a shared key between the base station and all sensors
[21]. Several random key predistribution schemes have been proposed

in the context of symmetric encryption techniques [13, 21, 37, 53]. In the
context of applying public-key cryptography techniques in sensor networks,
an efficient mechanism for public-key distribution is necessary as well. In
the same way that distributed sensor networks must self-organize to support
multihop routing, they must also self-organize to conduct key management
and building trust relation among sensors. If self-organization is lacking in a
sensor network, the damage resulting from an attack or even the hazardous
environment may be devastating.

4.6 Time Synchronization

Most sensor network applications rely on some form of time synchronization.
In order to conserve power, an individual sensor’s radio may be turned off
for periods of time. Furthermore, sensors may wish to compute the end-toend
delay of a packet as it travels between two pairwise sensors. A more
collaborative sensor network may require group synchronization for tracking
applications, etc. In [24], the authors propose a set of secure synchronization
protocols for sender-receiver (pairwise), multihop sender-receiver (for
use when the pair of nodes are not within single-hop range), and group
synchronization.

4.7 Secure Localization

Often, the utility of a sensor network will rely on its ability to accurately and
automatically locate each sensor in the network. A sensor network designed
to locate faults will need accurate location information in order to pinpoint
the location of a fault. Unfortunately, an attacker can easily manipulate nonsecured
location information by reporting false signal strengths, replaying
signals, etc.
A technique called verifiable multilateration (VM) is described in [81]. In
multilateration, a device’s position is accurately computed from a series of
known reference points. In [81], authenticated ranging and distance bounding
are used to ensure accurate location of a node. Because of distance
bounding, an attacking node can only increase its claimed distance from a
reference point. However, to ensure location consistency, an attacking node
would also have to prove that its distance from another reference point is
shorter [81]. Since it cannot do this, a node manipulating the localization
protocol can be found. For large sensor networks, the SPINE (Secure Positioning
for sensor NEtworks) algorithm is used. It is a three phase algorithm

based upon verifiable multilateration [81].
In [47], SeRLoc (Secure Range-Independent Localization) is described.
Its novelty is its decentralized, range-independent nature. SeRLoc uses locators
that transmit beacon information. It is assumed that the locators are
trusted and cannot be compromised. Furthermore, each locator is assumed
to know its own location. A sensor computes its location by listening for
the beacon information sent by each locator. The beacons include the locator’s
location. Using all of the beacons that a sensor node detects, a node
computes an approximate location based on the coordinates of the locators.
Using a majority vote scheme, the sensor then computes an overlapping antenna
region. The final computed location is the “center of gravity” of the
overlapping antenna region [47]. All beacons transmitted by the locators
are encrypted with a shared global symmetric key that is pre-loaded to the
sensor prior to deployment. Each sensor also shares a unique symmetric key
with each locator. This key is also pre-loaded on each sensor.
4.8 Authentication
An adversary is not just limited to modifying the data packet. It can change
the whole packet stream by injecting additional packets. So the receiver
needs to ensure that the data used in any decision-making process originates
from the correct source. On the other hand, when constructing the
sensor network, authentication is necessary for many administrative tasks
(e.g. network reprogramming or controlling sensor node duty cycle). From
the above, we can see that message authentication is important for many
applications in sensor networks. Informally, data authentication allows a receiver
to verify that the data really is sent by the claimed sender. In the case
of two-party communication, data authentication can be achieved through a
purely symmetric mechanism: the sender and the receiver share a secret key
to compute the message authentication code (MAC) of all communicated
data.
Adrian Perrig et al. propose a key-chain distribution system for their
μTESLA secure broadcast protocol [65]. The basic idea of the μTESLA
system is to achieve asymmetric cryptography by delaying the disclosure of
the symmetric keys. In this case a sender will broadcast a message generated
with a secret key. After a certain period of time, the sender will disclose the
secret key. The receiver is responsible for buffering the packet until the secret
key has been disclosed. After disclosure the receiver can authenticate the
packet, provided that the packet was received before the key was disclosed.

One limitation of μTESLA is that some initial information must be unicast
to each sensor node before authentication of broadcast messages can begin.
Liu and Ning [51, 52] propose an enhancement to the μTESLA system
that uses broadcasting of the key chain commitments rather than μTESLA’s
unicasting technique. They present a series of schemes starting with a simple
pre-determination of key chains and finally settling on a multi-level key
chain technique. The multi-level key chain scheme uses pre-determination
and broadcasting to achieve a scalable key distribution technique that is
designed to be resistant to denial of service attacks, including jamming.

Wireless Sensor Network Security - A Survey

2009-05-14T08:29:00.000-07:00

Abstract

As wireless sensor networks continue to grow, so does the need for effective
security mechanisms. Because sensor networks may interact with sensitive
data and/or operate in hostile unattended environments, it is imperative
that these security concerns be addressed from the beginning of the system
design. However, due to inherent resource and computing constraints,
security in sensor networks poses different challenges than traditional network/
computer security. There is currently enormous research potential in
the field of wireless sensor network security. Thus, familiarity with the current
research in this field will benefit researchers greatly. With this in mind,
we survey the major topics in wireless sensor network security, and present
the obstacles and the requirements in the sensor security, classify many of
the current attacks, and finally list their corresponding defensive measures.

2 Introduction

Wireless sensor networks are quickly gaining popularity due to the fact
that they are potentially low cost solutions to a variety of real-world challenges
[1]. Their low cost provides a means to deploy large sensor arrays
in a variety of conditions capable of performing both military and civilian

tasks. But sensor networks also introduce severe resource constraints due
to their lack of data storage and power. Both of these represent major obstacles
to the implementation of traditional computer security techniques
in a wireless sensor network. The unreliable communication channel and
unattended operation make the security defenses even harder. Indeed, as
pointed out in [65], wireless sensors often have the processing characteristics
of machines that are decades old (or longer), and the industrial trend is
to reduce the cost of wireless sensors while maintaining similar computing
power. With that in mind, many researchers have begun to address the
challenges of maximizing the processing capabilities and energy reserves of
wireless sensor nodes while also securing them against attackers. All aspects
of the wireless sensor network are being examined including secure and efficient
routing [15, 41, 62, 79], data aggregation [22, 33, 54, 68, 75, 91], group
formation [6, 42, 69], and so on.
In addition to those traditional security issues, we observe that many
general-purpose sensor network techniques (particularly the early research)
assumed that all nodes are cooperative and trustworthy. This is not the
case for most, or much of, real-world wireless sensor networking applications,
which require a certain amount of trust in the application in order to
maintain proper network functionality. Researchers therefore began focusing
on building a sensor trust model to solve the problems beyond the capability
of cryptographic security [23, 49, 48, 50, 70, 80, 90, 92]. In addition, there
are many attacks designed to exploit the unreliable communication channels
and unattended operation of wireless sensor networks. Furthermore, due to
the inherent unattended feature of wireless sensor networks, we argue that
physical attacks to sensors play an important role in the operation of wireless
sensor networks. Thus, we include a detailed discussion of the physical
attacks and their corresponding defenses [3, 4, 30, 34, 43, 71, 74, 84, 85, 88],
topics typically ignored in most of the current research on sensor security.
We classify the main aspects of wireless sensor network security into four
major categories: the obstacles to sensor network security, the requirements
of a secure wireless sensor network, attacks, and defensive measures. The
organization then follows this classification. For the completeness of the
chapter, we also give a brief introduction of related security techniques,
while providing appropriate citations for those interested in a more detailed
discussion of a particular topic.
The remainder of this chapter is organized as follows. In Section 3,
we summarize the obstacles for the sensor network security. The security
requirements of a wireless sensor network are listed in Section 4. The major

attacks in sensor network are categorized in Section 5, and we outline the
corresponding defensive measures in Section 6. Finally, we conclude the
chapter in Section 7.

3 Obstacles of Sensor Security

A wireless sensor network is a special network which has many constraints
compared to a traditional computer network. Due to these constraints it
is difficult to directly employ the existing security approaches to the area
of wireless sensor networks. Therefore, to develop useful security mechanisms
while borrowing the ideas from the current security techniques, it is
necessary to know and understand these constraints first [10].

3.1 Very Limited Resources

All security approaches require a certain amount of resources for the implementation,
including data memory, code space, and energy to power the
sensor. However, currently these resources are very limited in a tiny wireless
sensor.
• Limited Memory and Storage Space A sensor is a tiny device with
only a small amount of memory and storage space for the code. In
order to build an effective security mechanism, it is necessary to limit
the code size of the security algorithm. For example, one common
sensor type (TelosB) has an 16-bit, 8 MHz RISC CPU with only 10K
RAM, 48K program memory, and 1024K flash storage [14]. With
such a limitation, the software built for the sensor must also be quite
small. The total code space of TinyOS, the de-facto standard operating
system for wireless sensors, is approximately 4K [32], and the core
scheduler occupies only 178 bytes. Therefore, the code size for the all
security related code must also be small.
• Power Limitation Energy is the biggest constraint to wireless sensor
capabilities. We assume that once sensor nodes are deployed in a
sensor network, they cannot be easily replaced (high operating cost)
or recharged (high cost of sensors). Therefore, the battery charge
taken with them to the field must be conserved to extend the life
of the individual sensor node and the entire sensor network. When
implementing a cryptographic function or protocol within a sensor

node, the energy impact of the added security code must be considered.
When adding security to a sensor node, we are interested in the impact
that security has on the lifespan of a sensor (i.e., its battery life). The
extra power consumed by sensor nodes due to security is related to the
processing required for security functions (e.g., encryption, decryption,
signing data, verifying signatures), the energy required to transmit the
security related data or overhead (e.g., initialization vectors needed
for encryption/decryption), and the energy required to store security
parameters in a secure manner (e.g., cryptographic key storage).

3.2 Unreliable Communication

Certainly, unreliable communication is another threat to sensor security.
The security of the network relies heavily on a defined protocol, which in
turn depends on communication.
• Unreliable Transfer Normally the packet-based routing of the sensor
network is connectionless and thus inherently unreliable. Packets
may get damaged due to channel errors or dropped at highly congested
nodes. The result is lost or missing packets. Furthermore, the unreliable
wireless communication channel also results in damaged packets.
Higher channel error rate also forces the software developer to devote
resources to error handling. More importantly, if the protocol lacks
the appropriate error handling it is possible to lose critical security
packets. This may include, for example, a cryptographic key.
• Conflicts Even if the channel is reliable, the communication may still
be unreliable. This is due to the broadcast nature of the wireless sensor
network. If packets meet in the middle of transfer, conflicts will occur
and the transfer itself will fail. In a crowded (high density) sensor
network, this can be a major problem. More details about the effect
of wireless communication can be found at [1].
• Latency The multi-hop routing, network congestion, and node processing
can lead to greater latency in the network, thus making it
difficult to achieve synchronization among sensor nodes. The synchronization
issues can be critical to sensor security where the security
mechanism relies on critical event reports and cryptographic key distribution.
Interested readers please refer to [78] on real-time communications
in wireless sensor networks.

3.3 Unattended Operation

Depending on the function of the particular sensor network, the sensor nodes
may be left unattended for long periods of time. There are three main
caveats to unattended sensor nodes:
• Exposure to Physical Attacks The sensor may be deployed in an
environment open to adversaries, bad weather, and so on. The likelihood
that a sensor suffers a physical attack in such an environment
is therefore much higher than the typical PCs, which is located in a
secure place and mainly faces attacks from a network.
• Managed Remotely Remote management of a sensor network makes
it virtually impossible to detect physical tampering (i.e., through tamperproof
seals) and physical maintenance issues (e.g., battery replacement).
Perhaps the most extreme example of this is a sensor node
used for remote reconnaissance missions behind enemy lines. In such
a case, the node may not have any physical contact with friendly forces
once deployed.
• No Central Management Point A sensor network should be a
distributed network without a central management point. This will
increase the vitality of the sensor network. However, if designed incorrectly,
it will make the network organization difficult, inefficient, and
fragile.
Perhaps most importantly, the longer that a sensor is left unattended the
more likely that an adversary has compromised the node.

Bridging The Gap Between Wireless Sensor Networks And The Scientists Who Use Them

2009-04-24T20:08:00.000-07:00

A new, simpler programming language for wireless sensor networks is designed for easy use by geologists who might use them to monitor volcanoes and biologists who rely on them to understand birds' nesting behaviors, for example. Researchers at the University of Michigan and Northwestern University have written the language with the novice programmer in mind.

"Most existing programming languages for wireless sensor networks are a nightmare for nonprogrammers," said Robert Dick, associate professor in the U-M Department of Electrical Engineering and Computer Science. "We're working on ways to allow the scientists who actually use the devices to program them reliably without having to hire an embedded systems programming expert."

Finding an embedded systems expert to program a sensor network is difficult and costly and can lead to errors because the person using the network is not the person programming it, Dick said. The cost and disconnect associated with the situation means these networks aren't being used to their full potential.

Lan Bai, U-M doctoral student in electrical engineering and computer science, will present a paper on the new programming languages on April 13 at the Conference on Information Processing in Sensor Networks in St. Louis.

Modern wireless sensor networks, which have become more common in the past five years, allow researchers to monitor variables such as temperature, vibration and humidity in real time at various points across a broad environment. The sensors range in size from several centimeters across to several inches. Unlike passive radio frequency identification, or RFID tags, these active sensors can compute and communicate with each other through radio.

Civil engineers are working on using wireless sensor networks to monitor vibration in bridges to keep tabs on their health.

To create their language, the researchers examined the variables that a scientist using a sensor network might want to monitor, and the areas in which the scientist might need flexibility. They identified 19 of these "application level properties." They then grouped them into seven categories, or archetypes. They've essentially broken up the main programming language into seven archetypes that zero in on specific types of monitoring that different researchers might use. They wrote a language for one archetype and are working on others. They call their first language WASP, which stands for Wireless sensor network Archetype-Specific Programming language.

In WASP, scientists tell the system what they want it to do, rather than how they want it to complete the task.

"Scientists enter the requirements and our system sorts out the implementation details for them automatically," Dick said.

In a 56-hour, 28-user study that they believe to be the first to evaluate a broad range of sensor network languages, the researchers compared novice programmers' experiences with WASP and four common, more complicated languages.

On average, users of other languages successfully completed assigned tasks only 30 percent of the time. It took those who succeeded an average of 22 minutes to finish. When using WASP, the average success rate was 81 percent, and it took those who succeeded an average of 12 minutes. That's a speed improvement of more than 44 percent.

The paper is called "Archetype-Based Design: Sensor Network Programming for Application Experts, Not Just Programming Experts." Peter Dinda, an associate professor at Northwestern University, is a co-author with Dick and Bai. This research is funded by the National Science Foundation.

Temperature Sensor Types

2009-04-22T18:59:00.000-07:00

Big differences exist between different temperature sensor or temperature measurement device types. Using one perspective, they can be simply classified into two groups, contact and non-contact. The two links below take you to descriptive pages on each type with a breakdown by more specific, detailed types under that simple, first breakout.

There are also vendors of each sensor type, some vendors sell more than one type and some sell nearly all types, but not always all brands. There are differences between brands and the differneces are most evident among those device types for which there are few if any recognized standards. Start your search either for a specific temperature measurement device type or go to the vendor page index and you can access the vendors of specific types from there.

Both contact and non-contact sensors require some assumptions and inferences in use to measure temperature. Many, many well-known uses of these sensors are very straightforward and few, if any, assumptions are required.

Other uses require some careful analysis to determine the controlling aspects of influencing factors that can make the apparent temperature quite different from the indicated temperature.

Tell your new product and application stories at The Temperature Community website: www.tempsensor.net or feedback to us and we'll consider adding it here with your byline!

Remember the truism that all sensor have errors in their readings - all the time. One key secret to high quality measurement results is to have confidence in the error estimates. Neglecting to make a careful error analysis can result in error much larger than the assumed values.

It is worth noting that all competent error analyses start with the uncertainties assigned to the traceable calibration of the sensor itself. Without traceable calibration, one is forced to make assumptions. (You know what the word ass|u|me means, we hope.)

Without traceable measurements, the numerical values of results will always be questionable and hardly worth the effort, and cost. It most often pays to get started on the right path to technically sound measurements by beginning with some understanding of the options involved in selecting a temperature measurement device and then in obtaining one that meets the expected conditions and standards, is calibrated and that the calibration is traceable to either a fundamental standard (e.g. the triple point of water) or a national standard. See our calibration and standards pages for more details on each aspect of sound measurement practice.

Contact Sensors

Contact temperature sensors measure their own temperature.

One infers the temperature of the object to which the sensor is in contact by assuming or knowing that the two are in thermal equilibrium, that is, there is no heat flow between them.

Noncontact Sensors

Most commercial and scientific noncontact temperature sensors measure the thermal radiant power of the Infrared or Optical radiation that they receive from a known or calculated area on its surface, or a known or calculated volume within it (in those cases where the obect is semitransparent within the measuring wavelength passbad of the sensor).

One then infers the temperature of an object from which the radiant power is assumed to be emitted (some may be reflected rather than emitted). Sometimes the inference requires a correction for the spectral emissivity (NB: the two words, spectral & emissivity, are used together in correcting IR Thermometer readings -the "emissivity", unspecified, is a big trap which even some of the suppliers of devices and calibration equipment fall into unwittingly for a variety of reason about which one can only speculate ) of the object being measured.

Knowing how and when to apply a spectral emissivity correction is part of the inference, too, and can introduce significant errors if not done correctly. See our Trip down the E-missivity Trail to help you understand that aspect a little better.

Dewpoint Temperature

-- Humidity--

Although this area is in reality just an application of temperature sensors and other sensors, it grew out of temperature measurements.

Remember the old style humidity indicators that consisted of two little glass thermometers, the wet and dry bulb thermometers with a little look up table that told you the humidity, both absolute and relative? Have a look, it's a very important area in terms of human comfort, food safety and energy conservation and efficiency in thermal processes.

Thermal Imaging

The special world of thermography and thermal images includes the temperature-measuring kind of thermal imagers called "Radiomatic", by those in the business, and "Quantitative" by those mostly in R&Dwith thermal imaging. Then, too, there are those who call it "Thermology" when it applies to measurements made on the human body and "Medical Thermography" by still others, some even in the same business.

Users of infrared thermal imaging have many options in cameras both with and without temperature scales or temperature indication.

It seems really odd to have all these different names kicking about, when they all refer to the same basic technology. The names seem to differ only by application area. In reality, they all work because of the same Law of Physics, called Planck's Law.

That's the same law that describes how IR thermometers, optical pyrometers, radiation thermometers and infrared intrusion or people detectors work (note the common trait of multiple names).

The only thing that an IR thermal imager of any denomination really does is take the output from an infrared detector, or plethera of detectors, and presents a 2-D scan of the infrared intensity distribution in the field of view of an optical system. These devices could be called by one common name. The devices that provide temperature information, probably more than any other type of device should be called Infrared Imagers, or Thermal Infrared Imagers or, simply, Thermal Imagers.

Sensor Networks : An Overview

2009-04-16T19:23:00.000-07:00

Sensor network s are dense wireless networks of small, low-cost sensors, which collect and disseminate environmental data. Wireless sensor networks facilitate monitoring and controlling of physical environments from remote locations with better accuracy. In this paper, the authors provide an overview of overcoming various energy and computational constraints deficiencies through more energy efficient routing, localization algorithms, system design and the solutions proposed in recent research literature.

A sensor network can be described as a collection of sensor nodes which co-ordinate to perform some specific action. Unlike traditional networks, sensor networks depend on dense deployment and co-ordination to carry out their tasks.

Sensor networks have a variety of applications. fro examples include environmental monitoring, condition based maintenance, habitat monitoring, seismic detection, military surveillance, inventory tracking,etc.

Challenges :
In spite of the diverse applications, sensor networks pose a number of unique thechincal challenges :

Ad Hoc Deployment : Most sensor nodes are deployed in regions which have no network infrastructure at all.
Unattended operation : In most cases, once deployed, sensor networks have no human intervention.
Untethered : The sensor nodes are not connected to any anergy source. There is only a finite source of energy.
Dynamic changes : It is required that sensor network system can be adaptable to changing connectivity as well as changing environmental stimuli.

The authors survey a number of papers that propose solutions in these following areas :
- Energy efficiency
- Localization
- Routing

Energy Efficiency :
Energy consumption is the most factor to determine the life of a sensor network because usually sensor nodes are driven by battery and have very low energy resources. This makes energy optimization more complicated in sensor networks because it involved not only reduction of energy consumption but also prolonging the life of the network as much as possible.
The power consumed by the sensor nodes can be reduced by developing design methodologies and architectures which help in energy aware design of sensor networks.

A sensor node usually consists of four sub-systems :

a computing subsystem : It consist of a microprocessor (MCU)
a communication subsystem : It consists of a short range radio
a sensing subsystem : It consists of a group of sensors and actuators and link the node to the outside world.
a power supply subsystem : It consists of a battery which supplies power to the node.

Localization :
In sensor networks, nodes are deployed into a an unplanned infrastructure where there is no a priori knowledge of location. The problem of estimating spatial-coordinate of the node is referred to as localization. Some solution is use Global Positioning System (GPS), recursive trilateral/multilateral techniques, proximity based localization.

Routing :
Conventional routing protocols have several limitations when being used in sensor networks due to the energy constrained nature of these networks. These protocols essentially follow the flooding technique in which a node stores the data item it receives and then sends copies of the data item to all its neighbors. There are two main deficiencies to this approach :
-Implosion
- Resource management.
Some of the routing protocol which have been proposed for sensor networks aimed at eliminating these problems are :
- Negotiation based protocols - Sensor Protocols for Information via Negotiation (SPIN)
- Directed Diffusion
- Energy Aware Routing
- Rumor Routing
- Multipath routing
- Media Access Control in Sensor Networks

Simulators for Sensor Networks :
- NS-2
- GloMoSim
- SensorSim

Fibre Optic Temperature Sensors

2009-04-15T19:26:00.000-07:00

Introduction to Fibre Optic Temperature Sensors

Fibre optic sensors are a family of sensors (temperature is not the only parameter that can be measured) that employ thin glass fibres as the sole means to excite and read the sensing element. The fibre is the same as used for communications.

Characteristics of fibre optic sensors

There are several characteristics of optical fibres that allow them to be used for sensors. These include micro bending, interferometric effects, refractive index change, polarization change, fibre length change, fibre diffraction grating effects, and the Sagnac effect (light traveling in opposite directions around a loop used to sense rotation).

For fibre optic temperature sensors, the sensing element is usually deposited directly on the cleaved end of an optical fibre and temperature is deduced from the reflected phase or spectrum.

Another approach is to use a bimetallic strip (or other temperature sensitive mechanism) to bend the glass fibre sufficiently to generate a measurable anomaly. This is crudely demonstrated in the following diagram:

Fibre optic temperature sensors and their associated measuring devices are expensive and hence applied only when they have a compelling advantage for specialist applications. For this reason they are not comprehensively covered in this document. It is possible that in the future these types of sensors will become significantly cheaper and more widely deployed as optical integrated circuits become a reality.

Great for measuring temperature in difficult or dangerous places

Sometime temperatures need to be measured in places that do not allow conventional sensors to be employed. For example when measuring the temperature of the windings of a high voltage oil cooled power transformer. The voltage may be as high as 500 kV peak. Wired sensors would be a health hazard to anyone near the measuring device. However non-contact sensors cannot be used, because the transformer windings are not visible. It is situations like these that fibre optic sensors become the only option.