Beep Beep - Mote Runner has arrived!

Mote Runner is a run-time environment for mote-class wireless sensor networks (WSN) designed by IBM’s Zürich Research Laboratory. It consists of an on-mote run-time platform based on a virtual machine introducing its own byte-code language, tools (e.g., converter, assembler) to develop mote applications in Java and C# including plug-in integration with Eclipse (for Java) and Visual Studio (for C#), a mote and network simulation environment to facilitate application development, and a Web-based deployment and monitoring framework.

The IBM Mote Runner run-time environment for WSNs, currently under development, tackles these challenges in a holistic manner. Thus, at its core, Mote Runner provides a high-level, language-friendly, resource-efficient and high-performance virtual machine that shields portable applications from hardware specifics.

Currently, IBM Mote Runner runs exclusively on Crossbow's IRIS Mote platform. The IRIS mote comprises of an Atmel ATmega1281 processor, an Atmel RF230 radio controller for 2.4 GHz communication in accordance with to IEEE 802.15.4, 128 KB of program flash memory, and 8 KB of RAM. Crossbow offers various sensor boards for the IRIS platform, of which Mote Runner currently supports the MTS420 partially, namely its dual-axis accelerometer and the relative humidity / temperature sensor.

The platform supports software development in C# and Java, albeit it only supports a subset with limited functionality. For instance, it supports no threads. Nevertheless, the software environment can be configured dynamically and be reconfigured in the field. And the virtual machine makes sure applications can be moved to motes with different hardware. The development team hitherto worked in stealth mode. But the platform will be available "in the near future", Kramp asserted.

The core requirements to reap the promised benefits of a fully business-process-integrated infrastructure for deploying large numbers of sensors and actuators are security and end-to-end optimizations for such systems. This requires a well-designed ecosystem comprising inexpensive devices, as well as simple and bullet-proof device programmability for easy integration and use by specialists of the application domain, not of the device technology.

The IBM Mote Runner system addresses these challenges with a high-performance, low-footprint, standards-based software middleware platform comprising a hardware-agnostic and language-independent virtual machine together with development and integration tooling to easily create and manage applications for open sensor and actuator networks.

For more details on MoteRunner, visit the project site here.

Landslide Detection for Mountainous Regions

The Times of India reported today on a wireless sensor solution developed by students of Amrita Vishwa Vidyapeetham University for landslide detection. This development effort used Crossbow's MICAz Mote platform and was done in collaboration with the European commission and Indian Space Research Organisation (ISRO). This pilot deployment of India's first landslide detection system with wireless sensor networks was put in place at Munnar, Idukki, Kerala, India. This implementation brought together scientists from diverse fields such as geology and geophysics, mechanical,computer, electrical, electronics, and communication engineering to save human lives, preserve the environment, and mitigate property damage.

The devastation and loss of life caused by landslides affects hundreds of people every year around the world. Amrita University's rainfall induced landslide detection system uses a heterogeneous network that included wireless sensor networks in combination with Wi-Fi and satellite technology. The pilot site chosen for this study is highly prone to landslides due to systemic monsoon induced rainfalls in the region.

"Landslides occur frequently in mountainous terrains, especially during monsoons but detecting them in advance is not an easy task,'' says Dr P Venkat Rangan, vice chancellor, Amrita university. An expert in wireless communication, Rangan led a team of students in developing the model that has become operational in Munnar town in the Idukki district of Kerala.

This breakthrough technological system was developed as part of the research project WINSOC (Wireless Sensor Network with Self-Organisation Capabilities for Critical and Emergency Applications). Wireless panels with sensor nodes to read different soil parameters such as moisture, vibration and movement were embedded 15 metres beneath the earth at different points, says Maneesha Ramesh, a faculty member, who was part of the project.

The actual deployment site in the Idukki district built on the existing setup at Munnar as it provided the infrastructure needed for retrieving geological and hydrological data from the field as it was necessary for the data to be transmitted a long distance for further analysis. The data received from the geophysical sensors were transmitted through the wireless sensor network which used a two layer hierarchical topology.

The sensors were attached to a wireless transmission device, in this case Crossbow's MICAz Mote platform, which would then convert the analog value into a digital value and send the inputs to the base stations, which were connected to Amrita mutt's Kollam campus. "Experts will be monitoring the inputs from the base stations in real time and any unusual behaviour or extreme value will trigger an alarm,'' says Dr Rangan.

Multiple sets of geophysical sensors are located in a distributed manner inside a column, referred to as the 'sensor column'. The sensor columns are approximately 5 -6 meters long and are buried deep inside the earth and the data from them are retrieved using lower layer wireless sensor nodes attached to the sensor columns.

The lower layer wireless sensor nodes were wirelessly connected to a hierarchy of upper level wireless nodes that would forward the data on to a Gateway. The data was then sent via a directional Wi-Fi link to a Field Data Management Center (FMC). The data was then forwarded over a satellite link to the Data Management Center (DMC) which has sophisticated landslide data processing and modeling capability, located at Amrita University, Amritapuri campus which is situated approximately 252 kilometers away from the deployment field.

The fully tested model has become operational in Munnar. The system can be deployed in any part of the country prone to landslides and snow avalanches. The application could also be put to industrial use for the study of gas leakages or in conservation of forests by early identification of forest fires during summer.

As part of this project, representatives from various European partners like University of Rome, Selex Communications, Intracom Telecom, Czech Centre for Science and Technology arrived at the Amrita University to learn about the first-ever wireless sensor network system for landslide detection. The Amrita wireless sensor network system for landslide detection has been developed as part of WINSOC which is co-funded by INFSO DG of European Commission.

eKo network at NRS reserves pioneer new ways to observe Earth!

Crossbow's revolutionary eKo system was featured in the Spring/Summer Edition of Transect. The main article focuses on the implementation of sensor networks for observation. The eKo system is being used to monitor the microclimates of the various wetlands at the Blue Oak Ranch Reserve. The goal of the deployment is to collect detailed and accurate measurements about the environment to track changes, but also determine how these changes affect the plant life and various species within that ecosystem.

Reserve Director Mike Hamilton was looking to draw on the Blue Oak Ranch Reserves proximity to the Silicon Valley to collaborate on this product development and stated how products from Crossbow and other technology companies have been deployed, "...to show different applications for the tools and relevant
applications... so we’re teaming them with faculty and students from UC Merced to monitor wetlands that support salamander populations by deploying sensor networks to measure the changes in rainfall, soil moisture, water depth, and some of the chemical parameters of the water, such as salinity, that vary
across the reserve’s ponds, depending on soil type and water source. They all have different populations of amphibians, and they’re going to be different from pond to pond, so if we can set up these test beds in a few different wetlands, we can do comparisons across the reserve. We also want to test the reliability of the systems because, in the future, the reserves will want to pick those that prove their worth.”

Using various soil moisture sensors and ambient temperature/humidity sensors with the eKo node, researchers are able to gather valuable data quickly and easily. With its ecofriendly solar-panel and weatherproof enclosure, the eKo system takes technology into the wild! Using the advancements in networking technology, engineers and scientists working at the University of California, NRS reserves are playing a key role in the global discovery occurring through monitoring. The "Alpha Node" tower at Blue Oak Ranch provides information about data above ground and underground. As Hamilton states, "It’s a solar-powered weather station, but it’s also a wireless relay point that links the Lick Observatory [owned and operated by UC and located on nearby Mt. Hamilton*] to a directional Wi-Fi radio that points down to the barn, providing us with Internet access. And this omni-directional antenna plugs into the router on the tower to create a large Wi-Fi cloud on the top of the hill that’s strong enough to get a signal down to the pond and the stream at the foot of the hill, so researchers will be able to monitor these locations using portable wireless environmental sensing systems.”

Much of this work is based on the CENS research done at the James Reserve. This research has had a major influence on ecological observatory networks throughout the world. “It’s such a huge field of integration of interdisciplinary science between engineers and computer scientists and environmental scientists,” notes Hamilton. “It seems that everyone is doing sensor networks today...There’s a lot of growth right now in using sensor systems for precision agriculture, ranging from viticulture to golf course irrigations. Those seem to be the big areas where embedded-sensing and mesh networks are playing out. Our field, ecological monitoring of microclimates across a diverse landscape, is a niche market." Hamilton discusses how these deployments reflect the change in sensor networks from engineering projects to commercial off-the-shelf solutions such as the eKo platform.  

To read the full Transect article visit the NRS site here. For more information on the eKo system contact Crossbow or click here.

Derby Days

This past Saturday, May 2nd, the 135th running of the Kentucky Derby took place at Churchill Downs in Louisville, Kentucky. In an improbable ending, Jockey Calvin Borel rode Mine That Bird - a 50-1 longshot - to a huge victory, coming from dead last to win by 6 3/4 lengths. The Kentucky Derby is often billed as "the most exciting two minutes in sports", and Borel and Mine That Bird did their best to live up to that standard. 

The power and force exerted by these animals in those 2 minutes is amazing! A robotic hoof mechanism was shown at the derby. The image below shows the device clad with an aluminum shoe. The mechanism simulates the force, angle and impact of a racehorse hoof, and makes measurements to help detect trouble spots on tracks. Professor Mick Peterson demonstrated the machine while testing the racing surface at Churchill Downs on Saturday, April 25th, the week before the race.

The device used one of Crossbow's accelerometers to collect the data necessary to make these measurements. These accelerometers provide superior performance in small packages. With expertise in MEMS (Micro-Electro-Mechanical Systems) and DSP (Digital Signal Processing) technology, Crossbow accelerometers deliver reliable, cost-effective solutions across a wide range of applications. Several different accelerometers are offered, each optimized to meet customer needs in targeted fields.

Prof. Mick Peterson's research at University of Maine on Animal Biomechanics takes engineering technology and applies it to real life situations. Creating the robotic hoof allows owners the comfort of knowing that the track is safe for the horses to race on, offers them a playing field to encourage optimal performance and provides a fair and consistent racing surface to all riders. The device allows owners to understand the exercise impact of various tracks on the bone density of the horses, and the modeling done by the machine suggests that the device measures soil properties more than 1 foot beneath the track's surface. For more information on this research visit Professor Peterson's site here and for details on Crossbow's accelerometers, click here.

Congratulations to Kentucky Derby Winner Mine Than Bird and Borel for an amazing win!

Testbed Testing

Wireless sensor network testbeds are critical for understanding and meeting the technical challenges of networks deployed in real world scenarios. Hardware and software testbeds have become the preferred basis for experimenting with embedded wireless sensor network applications. They provide a means for developing and evaluating sensor network technology in a controlled and instrumented environment. Experimentation with current hardware and software platforms, allows users to not only demonstrate applicability in real environments but also to validate prototypes. Compared to field deployments, the testbeds yield substantial efficiency in instrumenting potentially long-lived experiments, which is valuable in the debugging, validation, and integration phases of reliable wireless sensor networks. Universities and labs across the world have set up networks of hundreds of nodes using a Mote platform from Crossbow's suite of wireless sensor network devices choosing from simple platforms such as the TelosB to advanced devices like the Imote2.

Researchers at the University of Buffalo, SUNY and Georgia Institute of Technology have written an article focused on the the idea of taking wireless testbeds to the next level by incorporating multimedia and characterizing the challenges of wireless multimedia sensor networks (WMSNs). The availability of low-cost hardware has enabled the development of WMSNs, i.e., networks of resource-constrained wireless devices that can retrieve multimedia content such as video and audio streams, still images, and scalar sensor data from the environment, along with standard wireless sensor networks. Research is being conducted on prototypes of multimedia sensors and their integration into testbeds for experimental evaluation of algorithms and protocols for WMSNs. Open research issues and future research directions, both at the device level and at the testbed level, are discussed and tested constantly.  Network testbeds allow the effectiveness of algorithms and protocols to be evaluated by providing a controlled environment for measuring network performance.

Every testbed utilizes a specific Mote platform that is optimized for that particular testbed's focus. The wireless sensor platform is chosen based on its available capabilities to allow for multimedia integration. A WMSN is a distributed wireless system that interacts with the physical environment by observing it through multiple media. Furthermore, it can perform online processing of the retrieved information and react to it by combining technologies from diverse disciplines such as wireless communications and networking, signal processing, computer vision, control, and robotics. Applications in the real world that would benefit from WMSNs were categorized into the categories of surveillance, traffic monitoring and enforcement, personal and health care, gaming, and several elements of environmental and industrial monitoring by researchers at SUNY, Buffalo and Georgia Tech. Testbeds allow the observation of the performance of the WMSN in a controlled environment. Hence, the effect of different types of inputs, physical operating conditions, and subjects for sensing can be studied, and the functioning of the devices in the testbed may be changed appropriately for accurate measurement. A few WMSNs developed are outlined below:

A visual sensor testbed was developed as part of the Meerkats project to measure the tradeoff between power efficiency and performance. Results revealed that there was significant energy consumption in keeping the camera active, and writing the image to a Flash memory followed by switching the camera off conserved energy. There was also a finite instantaneous increase in the energy consumption due to state transients. Researchers were also able to determine that the processing-intensive benchmark resulted in the highest current requirement, and transmission was shown to be only about 5% more energy-consuming than reception.

Expandable, vision-, and sensor-equipped wireless robots with MICA sensor motes for networking were designed in the Explorebots testbed architecture. The target localization experiments on the testbed, composed of these mobile robots, used on board multimedia sensors such as custom-designed velocity and distance sensors, motor movement control, an in-built magnetic two-axis compass, and sonic sensors. By processing the sound and light sensors outputs, the robots were guided towards the target source.

The Mobile Emulab network testbed provided a remotely accessible mobile wireless and sensor testbed. Robots carried motes and single-board computers through an indoor field of sensor-equipped motes. A remote user could position the robots, control all the computers and network interfaces, run arbitrary programs, and log data in a database. The path of robots, which was also equipped with Webcams, could be planned, and a vision-based system provided positioning information with an accuracy within 1 cm.

IrisNet (Internet-scale resource-intensive sensor network services) was an example software platform for a heterogeneous WMSN testbed. Video sensors and scalar sensors were spread throughout the environment and collected potentially useful data. IrisNet allowed users to perform Internet-like queries to video and scalar sensors that spread throughout the environment. The architecture of IrisNet was two-tiered: heterogeneous sensors implemented a common shared interface and were called sensing agents (SAs), while the data produced by sensors was stored in a distributed database that was implemented on organizing agents (OAs). Different sensing services were run simultaneously on the architecture. For example, a set of video sensors could provide a parking-space finder service, as well as a surveillance service.

In the design and implementation of SensEye, a multiple-tier network of heterogeneous wireless nodes and cameras, was created to test surveillance applications. Each tier comprised nodes equipped with similar cameras and processing ability, with increasing resolution and performance at each stage. The lowest tier consisted of low end devices, i.e., MICA2 Motes equipped with 900 MHz radios interfaced with scalar sensors, e.g., vibration sensors. The second tier was made up of motes equipped with low-fidelity Cyclops or CMUcam camera sensors. The third tier consisted of Stargate nodes equipped with Webcams that could capture higher fidelity images than tier 2 cameras. Tier 3 nodes also performed gateway functions, as they were endowed with a low-data-rate radio to communicate with motes in tiers 1–2.The aim was to efficiently undertake object detection, recognition and tracking by triggering a higher tie into the active state based on a need basis.

The WMSN-testbed at the Broadband Wireless Networking (BWN) Laboratory at Georgia Tech was based on commercial off-the-shelf advanced devices and had been built to demonstrate the efficiency of algorithms and protocols for multimedia communications through wireless sensor networks. The testbed was integrated with the scalar sensor network testbed, which was composed of a heterogeneous collection of Imote and MICAz motes from Crossbow. The testbed allowed the integration of heterogeneous devices in experimental testbeds and some succesful examples in developing APIs and system software for WMSNs.

These are just a few of the various wireless sensor network testbeds found worldwide. Crossbow's vast portfolio of wireless sensor platforms provides researchers and government/commercial users with the equipment they need to set up a lab for their wireless sensor course or to verify their specifications prior to real world deployment of their wireless sensor network. For information on how to set up your own WSN testbed or for details on Crossbow's wireless sensor network platforms, contact sales@xbow.com.

Professors design AK47-locating 'smart dust' helmets

Last week Vanderbilt University's inhouse Exploration newsletter reported on the gunshot-locator net developed by the university's Institute for Software Integrated Systems (ISIS). Crossbow's Mote platforms were once again highlighted in their use for the shooter location system. Acoustic gunshot detectors have become common in the past few years, and some have been reduced in size to where a single soldier can wear one on his uniform and be cued-in to an enemy's location as soon as he fires. Engineers at ISIS have developed a system that can give soldiers just such an edge by turning their combat helmets into "smart nodes" in a wireless sensor network.

Current systems rely on centralized or stand-alone sensor arrays. This limits their accuracy and restricts them to identifying shooters at line-of-sight locations. By contrast, the ISIS system combines information from a number of nodes to triangulate on shooter positions and improve the accuracy of its location identification process. It also uses a patented technique to filter out the echoes that can throw off other acoustic detection systems, explains Akos Ledeczi, the senior research scientist at ISIS who heads up the development effort.

ISIS developed this novel technology with the support of the Defense Advanced Research Project Agency and the university has patented the system's key elements. "When DARPA gave us the assignment of creating a shooter location system using nodes with very limited capabilities, they didn't think we could solve the technical problems," Ledeczi admits. "At first, I didn't think we could do it either, but we figured out how to make it work!"

Retired U.S. Army Lieutenant Colonel Albert Sciarretta, who assesses new military technologies in urban environments for DARPA, is one of the experts who is impressed by the ISIS system: "It's strong points are that it isn't limited to locating shots fired in direct line-of-sight, it can pick up multiple shooters at the same time, and it can identify the caliber and type of weapon that is being fired."

"Because the microphones on the helmet are so close together, the precision is not very high," Ledeczi says. "However, the nodes are continuously exchanging the times and angles of arrival for these acoustic signals, along with their own locations and orientations. When two or more nodes detect the shot, they can provide the bearing with better than one degree accuracy. The range is typically within a few meters even from as far as 300 meters. The more sensors that pick up the shot, the more accurate the localization." The ISIS system communicates its findings with the personal digital assistants that the soldiers carry. The PDAs are loaded with maps or overhead pictures of the area upon which the shooter locations are displayed.

In each package is a wireless network node, of a type dubbed a "smart-dust mote" for its small size and cheapness. There are also four separated microphones, for picking up the acoustic signatures of flying bullets, and a GPS satnav location system. The GPS isn't accurate enough to act as a basis for properly pinning down opposing gunmen, so the Vanderbilt boffins added a crafty radio interferometry enhancement system of their own - apparently of such cunning that it has attracted as much interest as the rest of the system on its own.

The system works by picking up the distinctive conical shockwave trailing behind a passing supersonic bullet - the same phenomenon which produces a sonic boom behind a plane at Mach 1+. This is then related to the muzzle blast from the weapon which fired it, trailing slightly behind (the two noises are heard by people under fire as "crack-thud", or "crack-bang"). A software algorithm in the unit can work out a range and bearing to the enemy weapon's muzzle. A video of the trials can be seen on the Exploration site:

The ISIS shooter system uses the wireless nodes produced by Crossbow Technology Inc. These smart nodes, or motes, form self-organizing wireless-sensor networks and are the realization of the Pentagon's "smart-dust" concept of radically reducing the size and cost of sensor networks for military applications. Current commercial shooter location systems are extremely expensive, with prices ranging from $10,000 to $50,000 per unit. By contrast, an entire node for the ISIS system weighs only slightly more than the four AA batteries that power it and costs about $1,000 to construct using currently available commercial hardware.

The Exploration article is here, and a detailed paper from Ledeczi's team here.

GOOOOOOAAAALLLLL!!!!

Soccer (or as it is more widely known - football) has often been considered the most popular sport in the world. Wherever you go, there are kids on the street or in a park kicking around a ball, jerseys from the most popular football clubs can be seen in every major city, and the truest World Cup truly does include most of the nations on this earth. It seems fitting that Motes would enter the arena of sports through soccer. The idea behind Motes and wireless sensor networks is to create a device that will become ubiquitous throughout this planet enabling us to live in a smarter more connected world. Soccer has connected people from all over, at all ages with all levels of skill for generations, and now Motes have made their way into this arena with a little bit of fun!

Embedded systems are increasingly becoming connected through wireless networking.  These devices now form the basis of many of today’s consumer products including cell phones and video game controllers.  In the CSE466 “Software for Embedded Systems” class in the Department of Computer Science & Engineering at the University of Washington, students used the design of a multi-player video game as motivation for the  principal concepts in wireless embedded systems.  Each student in the class designed an accelerometer-based game controller and then, the class as a whole, developed a multi-player video game that allowed 28 players (the number of students in the course) to play simultaneously. How did they do it? With Motes!

Computer Engineering curricula have traditionally included the interfacing of sensing and actuation devices to microcontrollers but have not emphasized wireless communication. This time students at University of Washington undertook the task of updating the platform used in this course to Crossbow's Imote2 which runs an embedded Linux operating system and developing a multi-player video game using controllers modeled on the, then just introduced, Nintendo Wii video game controller. Previous courses had used the MICA Mote platform, but the Imote2 offered a device with greater capabilities.

Each student designed their own Wii-like accelerometer-based 2-D game controller.  An LCD screen was added so that game state could be displayed on the user’s controller during the game. The Imote2 platform offers users an expansion connector for attaching sensing and actuation hardware.  On one side, students attached the basic ITS400 Imote2 sensor board which includes a 3-axis accelerometer, temperature sensors, a humidity  sensor, a light sensor, and a 4-channel A/D for further additions.  On the other side, students designed their own board to provide some actuation known as the "SuperBird".  A cell phone-size color LCD screen as well as sound generation capabilities, a speaker, microphone, and audio jacks.  This board also included a cell phone camera, jog dial, USB host port, barometer, and a heart rate sensor and is the form factor of a large cell phone. While not all of the board’s capabilities were used in this particular application, the board was designed to be flexible enough to accommodate a variety of projects (such as a video phone, music player, etc.) in the future. The Imote2 platform is based on Linux and students were provided with the device drivers for their project implementation.

 

Students decided on utilizing controllers that could move a player in two dimensions.  Soccer quickly emerged as a game that could be varied to accommodate the project requirements, namely, that it should involve every student simultaneously and require only two-dimensional control of each player.  In addition, students wanted to have some collaborative elements between players that would spur real team play - soccer being a true team sport! The game was developed in steps.  Development began with the basic player controller, that is, the mapping of values from the accelerometer to an X-Y velocity vector.  A communication protocol needed to be devised on top of the basic MAC of the 802.15.4 radio to handle the communication between players and  the game coordinator.  To ensure that there was some inter-player communication, the “captain” of the merged players (the player with
the highest number) collected all the moves of the constituent players and reported that result to the game coordinator.  The scheme was basically round-robin.  The game coordinator polled the first player for its move and waited for a response before proceeding to the next player.  If a player was too slow in responding, its movement was ignored for that round.  This provided some timing constraints on the implementations to quickly handle packets from the game coordinator.  The boards were programmed to allow students to control players in the virtual soccer-like game. Tilting the board would cause the player (represented by a dot on the field) to go in the corresponding direction.

On the final day of the course a 30-minute soccer match was held between the two sections of the class.  A video of the match and an explanation of the project can be seen here:

GOOOOOAAAAAAAALLLLLL for CSE466 at University of Washington!!

Robocopters and Motes

Crossbow's Motes were featured last month in an article on IEEE Spectrum. The application featured showcases how the Motes can be used when deploying mobile distributed communications networks.

The meter-long helicopters lined up under the fluorescent lab lights at the Berlin Technical University might look like overgrown toys, but they’ve got a little more under the hood. These are flying robots. They take off, land, and scout terrain autonomously and are being wired to deploy ad hoc communications sensor networks. And unlike any other robocopters, they can work together.

Researchers expect they’ll be used to distribute sensors that would help coordinate firefighting efforts or search flood zones, to track or find people and vehicles, or to shoot movies and cover sports events. Hoisting communications gear, they could one day help channel radio, Wi-Fi, or mobile phone traffic where infrastructure has been wiped out by an earthquake or other natural disaster.

Several groups around the world are working on miniaturized robot helicopters with advanced intelligence, notably in California, South Carolina, the Netherlands, South Korea, and the UK. But the Berlin team believes it is the first to write software and build systems that get multiple robot copters to collaborate. The project brings together a half-dozen institutions and companies from across Europe.

The copters’ control systems allow small craft to work together in lifting loads and scouting environments. Coordinated, the copters can lift weights that would normally require larger, exponentially more expensive machines. Estonian robotics engineer Konstantin Kondak, a professor at TU Berlin and one of the project’s leaders, says that having three or four copters in the air, each sharing the load while tethered by a rope to a single object, creates too many contrary forces for a set of human pilots to handle: “If you try to do this flying manually, it is not a stable system. You have to correct at all times; it’s too much.” But autonomous robots, making instant and coordinated adjustments, can do the job, he says.

Each robot must account for the location of the other helicopters, the forces coming from them, and the load on the rope, to jointly lift something. The helicopters’ coordination comes from a system that integrates four software modules for stabilizing the copter: one for navigation, one for exploration, one for obstacle avoidance, and one for processing orientation, horizon, and position.

The robocopters are good for much more than just lugging things around. Project manager Aníbal Ollero, a professor of engineering and automated systems at the University of Seville, in Spain, says that a flexible, easy-to-transport team of choppers makes for more efficient scouting because they automatically divide an area among themselves.

They’re also faster at another important task: deploying distributed communications networks by dropping off sets of tiny sensor nodes. These nodes combine a data processor, a radio, a battery, and—depending on what needs measuring—temperature, light, radiation, location, or chemical sensors. For the autocopter project, off-the-shelf nodes from wireless-sensor firms Crossbow Technology and Ambient Systems were optimized and linked by data-routing experts at universities in Stuttgart, Germany, and Twente, Netherlands. Just a few centimeters across and 7 millimeters thick, the individual nodes transmit over a range of just 25 meters, but as a network they pass radio messages to one another to get to a central unit (or hovering robocopter). Hundreds of thousands of these nodes could be distributed by robot to survey a forest fire or flood zone for rescue efforts, according to the researchers.

Final trials for the project get under way, far from the Berlin winter—this spring in southern Spain, Ollero says. If all goes well, helicopters will deploy a sensor network, track mobile objects and people, follow movement inside and outside of buildings, and capture it all with high-end airborne cameras.

For more information on Crossbow's Mote platforms, visit www.xbow.com.

Sensors Help Keep the Elderly Safe, and at Home

The New York Times published a front page story regarding how sensor technology is changing the face of home health care. The article discussed how increasingly, many older people who live alone are not truly alone. They are being watched by a flurry of new technologies designed to enable them to live independently and avoid expensive trips to the emergency room or nursing homes.

Crossbow's wireless sensor network technology has been used in a plethora of various research and project deployments on the capabilities of integrating wireless sensor network technology for elder care. Recently, researchers at Kalasalingam University in Nadu, India published their project details in the Jan/Feb Issue of Telemedicine and eHealth on using the IRIS and Imote2 platforms for home health monitoring. The issue they address is real as the increasing demand on public health care due to the aging population has become a major problem in developed countries. With the increasing number of elders relying on home care, better monitoring and analysis systems are crucial for maintaining and improving the quality of life for the elder patients. The concept of health monitoring is advanced by which health parameters are automatically monitored at home without disturbing daily activities. The proposed system is a network that supports various wearable sensors and contains on-board general computing capabilities for individual event detection, alerts, and communications with various medical informatics services. The purpose of their system is to provide extended monitoring for elderly patients under drug therapy after infarction, data collection in some particular cases, and remote consultation for elderly people.

With the advancement in ubiquitous computing techniques, researchers at Kalasalingam University proposed this new system for health monitoring at home to assess the elderly peoples’ health status. The integration of sensing, information, and communication technologies allowed elderly people to be constantly monitored. Moreover, constant monitoring would increase early detection of adverse conditions and diseases in elderly patients, potentially saving more lives. The proposed healthcare system was built upon a global medical information system made up of three main components: (1) Various sensors of different types to monitor physiological signals of elderly patients, their environments, and their activities with sensor nodes kept in the patients’ home; (2) Sensor nodes to transmit the signal from sensors to central servers through a base station; and (3) Central server for management of a knowledge database related to patients and responsible for broadcasting messages and alarms to healthcare professionals and caretakers. The system proposed a technique for monitoring elderly patient status at home and detecting critical situations for the purpose of alerting the doctors as well as the caretakers. It consists of a front end, the patient station, and the server.

The Patient Station, consists of an IRIS (XM2110CA) sensor node, Crossbow's newest 2.4GHz Mote platform for ultra low-power, long-range wireless sensor networking that receives information from the physiological sensors. The 3D camera kept at home was connected with the Imote2 platform (IPR2400CA) to visually monitor the elderly patient in the home environment in addition to the physiological signals. The Imote2 sensor node is aimed at applications involving data-rich computations, where there is a need for both high performance and high bandwidth, which require greater processing capability and low-power operation with a low duty cycle to achieve longer battery life. Sensor nodes are responsible for sensing as well as the first stage of sensory data processing in the data communication. The received signal from the patient station was transmitted to the central server for analysis. Researchers found that the benefits of the IRIS mote included excellent radio frequency range, substantially lower sleep current, and double the program memory of other Mote offerings. By Integrating a high-performance, low-power PXA271 Intel processor and an 802.15.4 radio with a built-in 2.4-GHz antenna, the Imote2 provided a platform for digital imaging applications.

Researchers placed sensor nodes equipped with flex sensors on the chest for ECG, and on the right hand for heart rate and acceleration measurement. The IRIS sensor node placed on the body was responsible for collecting physiological data and movement data from the sensors and transmitting it to the central servers through the aggregation node. The aggregation sensor nodes do not act merely as data collecting and forwarding points. Instead, they make decisions whether the information should be stored for future use, relayed as they are, or modified by applying computation and aggregation with other data.

The server, which is the core processing element, received the patient’s data from the patient station through the base station and stored the data into the patient database, while performing long-term trend analyses and prediction. It dispatches the critical events detected by sensor nodes to healthcare professionals. Moreover, it coordinates and controls the overall functionality of the system. This system can also give the alert to the doctors via a personal digital assistant (PDA) or cell phone. The sensor nodes could be programmed to awaken the node whenever an abnormal signal is detected and transmit the data to the server and then return to sleep mode. When an abnormality was detected in the sensor node, it would automatically turn on and transmit the signal to the central server and simultaneously alert the caretaker near the patient. The server, which is the core processing element, received the data regularly from the sensor nodes for analysis. The analysis was done using Crossbow's MoteView, and operates under TinyOS, which offers a built-in library for data acquisition, processing, analysis, and display.

The central servers were programmed with two algorithms. First was the threshold based algorithm, which attempted to identify the physiological parameter values that were potentially harmful or indicative of immediate danger to the patients. The algorithm detected the upper and lower threshold values from sensors output and alerted medical personnel and caretakers when the patient was not physically capable of requesting help. Second was the inactivity detection algorithm for detecting rapid movement or lack of movement of elder patients from accelerator output. The sensor node was programmed through the central server in such a way that if lack of movement/ action is detected, it awakened the Imote2 device connected to a 3-D camera fixed in that specific room to send the status of the elder patient to a central server. Upon detecting an anomalous event, both algorithms would sound an auditory alarm from the central server to emergency departments and alert the caretakers near the patients.

In this implementation, researchers at Kalasalingam University were able to connect two different systems, that is, the patient record database and the Web portal, through the use of well-defined Web services. Patient information was transmitted over SOAP, a secure and encrypted form of XML. The WSDL (Web Service Definition Language) for these Web services is published to a community of authorized users. This Web-service based approach for inter-system communication gives the system the flexibility to operate with third-party software in the future.The proposed system used a wireless sensor network technology within the residence in order to increase functionality, security, and quality of life.

As the Times put it, 'The future of these technologies, and the terabytes they gather, can involve unprecedented information about the whereabouts and well-being of older people.'

LiteOS v. 1.0 Released with Support for IRIS Mote Platform

In July of 2007, Crossbow Solutions featured the development of LiteOS by researchers at the University of Illinois at Urbana-Champaign. LiteOS is a UNIX-like operating system that fits on memory-constrained devices like Crossbow's Mote platforms. This operating system allows users to operate wireless sensor networks like operating UNIX.

Version 1.0 of LiteOS has just been released. Now offering complete support for Crossbow's popular IRIS Mote platform, several new features have been implemented. Key Features in Version 1.0 include:

• Windows XP, Windows Vista and Linux Support
• Support for MICAz and IRIS nodes
• Plug-and play routing stack
• Extremely lightweight event logging
• Unix like commands to operate the entire sensor network
• Multi-threading kernel
• Write applications in C
• Native wireless reprogramming
• Built-in hierarchical file system
• Extensive development libraries
• Java tools to display and visualize data
• Online debugging support, including variable watches and unlimited number of breakpoints
• Elastic dynamic memory that has almost zero overhead
• Snapshot a thread state or restore it to a previous state
• Installer for quickly deploying the LiteOS operating system
• Documentation to quickly get started with operating and programming

The goal of LiteOS is to simplify sensor network programming. For more information on this OS, click here.