Ice scream, you scream...

Being a native California driver I rarely encounter icy road conditions unless I'm on my way to Tahoe to go snowboarding or skiing. I remember the day we had snow in the Bay Area and everyone ran outside to experience the phenomenon (although it only lasted on the ground for a few moments before melting away). In many parts of the world icy road conditions prevail and the ability to easily monitor and detect the danger is not easy due to the harsh environment. However, the idea of pavement condition monitoring would save many a spinning car and screaming driver from sliding on the ice into the side of the road.

Pavement maintenance is vital for travel safety. By using wireless sensor networks to monitor pavement temperature and moisture presence, icy road conditions can be detected. It is essential to provide warnings of dangerous traffic conditions in real-time. In a study done at University of Oklahoma, researchers determined to investigate a densely distributed sensor network and classify pavement conditions into certain categories - 1) dry 2) wet and 3) frozen.

This project was deployed with the MICA2 Motes from Crossbow while integrating them with various 3rd party sensing devices using the MDA100 prototyping board. The ability to integrate 'alien' sensors to the Mote platform gave the researchers the flexibility they needed to complete the task at hand. The sensors chosen to provide the data included a thermistor to gather temperature readings, a leaf sensor to detect the conductivity of a wet pavement to detect the existence of free moisture and an infrared sensor to detect ice by emitting a near infrared light that is reflected by the ice and detected by the infrared receiver (water is transparent to the receiver).

An integrated sensor and road button structure housed the 3 sensors as shown in the figure above. The top surface of the sensor road button contained the moisture and infrared sensors with the thermistor at the bottom. Due to the low power consumption of the sensors used, these devices were powered by the MICA2 Mote platform. The Mote platform was placed into a protective watertight aluminum casing with upgraded antenna doubling the Motes transmitting range.

When collecting readings from the sensors, the Mote transformed them into digitized data, sent them to the radio and waited until all data was sent before switching to sleep mode. In detail, the processor received sensor readings from the embedded 10-bit Analog-to-Digital Converter (ADC). If the data was taken correctly, the onboard light emitting diodes (or LEDs) lit up to signal the proper functioning of the mote. Analog to digital conversion was performed on the readings, after which the data was integrated into the packet to be transmitted. The default packet format was slightly modified to fit the size and format of the data to be transmitted. The packet was then sent to the radio and transmitted over the network until it reached the base station. The base station was connected to a laptop through a serial port. The data was then collected using a LabVIEW graphical user interface (GUI) developed for this project. Raw data from the serial port was collected, deciphered and displayed by the GUI.

Using the MICA2 Motes to monitor the pavement conditions is a unique application; therefore, the aforementioned features (directly applying time synchronization and embedding a pattern classification algorithm) further distinguish this study from existing research that utilizes Motes in real-world applications. A series of laboratory tests was conducted at the Asphalt Laboratory at the University of Oklahoma using an environmental chamber to study the effect of temperature and moisture on the sensors (and later, the motes). The environmental chamber was used to produce well-controlled temperature and humidity variations. The sensor-road button unit was tested to (1) test the full functionality when all the sensors were combined together, and (2) collect data to aid refinement and further development of the proposed ice detection algorithm proposed in this application. The entire lab test was completed in a four-hour time frame. Note that weather changes in reality could be much slower than this testing rate; thus such a test could be more stringent than a real-world situation.

A series of outdoor tests were conducted as well paying special attention to the packaging and survivability of fragile analog sensors in harsh roadway conditions and how they will be utilized in other applications of intelligent transportation systems (ITS) as well as structural health monitoring. These methods allowed the Mote wireless sensor network to be easily installed and provided a robust solution to environmental factors such as wind and rain. Imagine a day when roads are 'smart', when you are told exactly what conditions to expect before you encounter a patch of ice. It is this concept and future that we envision with the Mote platforms - a smarter safer future that we all can scream for!

Who needs The Club when you've got SVATS!

According a report by the FBI, a vehicle is stolen every 26.4 seconds in the United States. The western states account for the highest rate of thefts in the USA, and 4 of the top 10 metropolitan areas were in California - made me feel very safe! Remember 'the club' from back in the day? I remember watching the commercials when I was a kid between episodes of Saved by the Bell and thinking that my parents should get one - it seemed like the perfect solution. Check out this commercial from the nineties (love the hairstyles and outfits).



Luckily today, things have progressed, and instead of having to whip out your club and strap it to the steering wheel of your car, you can install SVATS. SVATS is a sensor-network-based vehicle anti-theft system based on Crossbow's MICA2 Mote platform. Conceptualized by researchers at Penn State University, SVATS is designed to address the limitations of high cost, high false-alarm rate and the easy disabling function of current tracking/alarming systems. In this system, the vehicles in an area are outfitted with a sensor node and form a wireless sensor network. The nodes in the network then monitor and identify possible vehicle thefts by detecting unauthorized vehicle movement. When an unauthorized movement is detected, an alert is sent to the base station which sends warning messages to the security office or whomever is responsible for that area. The security system relies on networks of cars constantly gossiping with their neighbors using the concealed wireless nodes. The cars raise the alarm when a thief tries to make a getaway.

With vehicles playing an essential part in our every day life, there are many solutions to stop theft from lock systems (like the club), alarm systems (that we all ignore nowadays) and vehicle tracking/recovery systems. Most of these tracking/recovery systems require the user to purchase the product as well as pay a monthly maintenance fee, or use GPS which does not work indoors or is easily located and disabled. SVATS proposes to have a each vehicle equipped with a node, and each parking area forming its own sensor network with base station. Each node is powered by the vehicle's power source and controlled by a remote so that the user can turn it on so that the node sends a 'join' message and broadcasts its 'alive' message periodically. If it does not send out a 'leave' message that is authenticated by the user via remote that turns the node off, the neighboring sensors will detect the movement or should they not receive the 'leave' message report the problem to the base station and owner via alert. To track the vehicle SVATS used roadside access points already deployed to determine where the vehicle had been moved to. The researchers themselves drove off some cars to test how the system worked, and found that SVATS detected all such "thefts" in a matter of just 4 to 9 seconds. The system was apparently resistant to false alarms caused by weather, or people walking around the car park, both of which can affect the signals between sensors.

SVATS included four components network topology management, vehicle theft detection, intra-vehicle networking, and alert reporting. Using the MICA2 Mote platform in the sensor node for this deployment, researchers were able to use the self-forming, ad-hoc capability of the Motes to allow the device to find its neighbors and join the network. The vehicle theft detection was done with two techniques - count-based and statistical-based. RSSI signals and values were also used to determine whether a vehicle had been stolen or not. The system can also detect when a car is moving unexpectedly by measuring the signal strength of any "alive" messages. If a car detects significant changes in signal strength, it sends a warning message to other cars monitoring the same vehicle, because it is likely to be moving. However, it is only when a watching car receives more than three such warning signals from different sources that it will send out a theft alarm message to the base station. Ensuring that multiple cars must agree on a threat before the alarm is raised should cut out the false alarms that plague other anti-theft systems, say the researchers. Experimental evaluation of the SVATS system used a laptop as a base station and one sensor per vehicle in a Penn State parking lot.  The base station transmitted once per second while the vehicle sensors sent live messages every 200 milliseconds.

The key to SVATS is that the sensor nodes are cheap and easy to deploy. They are designed to work in a large network that creates a smart and safe environment. This solution can be deployed incrementally and the rapid response time it provides is motivation enough to install the SVATS sensor nodes. This research was funded by NSF and the Army research office. The researchers presented information on their system at the Institute of Electrical and Electronic Engineer's Infocom 2008 Conference in Phoenix.  

As one person said, stealing a car wont be easy for thieves anymore, thanks to this new type of car alarm that enables the vehicles to look after each other"s safety - just like a herd of animals under any potential threat from predators.

Now that's a bright idea!

Presenting the new 'Ping-pong' Illumimote, a light sensing module for the Crossbow Mote platform. Wireless sensor networks have permeated the market in many sectors such as environmental monitoring, asset tracking, industrial automation, etc. But, these devices have exciting applications in arts, multimedia and entertainment as well. Limited sensing quality, fidelity and diversity of the sensor modules have limited their expansion into areas like film, video production and lighting control. To support research and development in these areas, high-fidelity light sensing modules in a compact form factor are required.

Enter the Ping-Pong Illumimote! This device achieves performance comparable to a commercial light intensity meter, while conforming to the size and energy constraints imposed by its application in wireless sensor networks. The board was developed to replace the light sensing capabilities on the MTS310 Mote sensor board whose response time and narrower dynamic range in light intensity capture is unsuitable to many certain applications for light measurement such as media production. The Illumimote features significantly improved SNR due to its adoption of high-end photo sensors, amplification and conversion circuits coupled with active noise suppression, application-tuned filter networks, and a noise-attentive manual layout. Unlike the MTS310, the Illumimote can capture RGB color intensity (for color temperature calculation) and incident light angle (which discerns the angle of ray arrival from the strongest source). The prototype was created by the UCLA NESL & the UCLA Hypermedia Studio, a joint effort between the film makers and the engineers, to apply wireless sensor networks to new purposes in art and entertainment.



The order in which an audience views a film’s sequence of events is remarkably different from the order in which they are produced. Shots are filmed in the order that minimizes cost and makes best use of actors, crew, and locations. Footage captured at these different times must appear the same when shown consecutively, or differences must be controllable if they are required for creative purposes. It is important to monitor and replicate the quality of light (illuminance and color) in each shot, so that footage captured at different times or in different locations doesn’t show unexpected differences, which may not be perceived by the human eye but affect the film stock. The Lord of the Rings trilogy, for example, was filmed over a year and a half of production and required that footage be captured for use in three different movies with vastly differing release dates and schedules. Researchers at UCLA have focused on lighting instrumentation as the first component of their Advanced Technology for Cinematography (ATC) because of its vital role in the creative process of filmmaking. ATC is a joint project of UCLA’s Henry Samueli School of Engineering and Applied Science and the School of Theater, Film and Television. The Illumimote is part of their larger vision to increase flexibility and creative control in media production using sensor networks and other emerging technologies. Deploying networks of tiny sensors adds a data acquisition layer to the film production environment that supports on-set decision making, such as the lighting adjustment described above, as well as post-production and asset management.

Initial work prompted the development of the Illumimote and other high-quality sensor platforms that can be deployed atop Mica Motes, the defacto standard for WSN nodes. The Illumimote is designed to have equal or better performance to the class of commercial light intensity and color temperature meters used in the entertainment, film and video production industries. It supports three different light sensing modalities: incident light intensity, color intensities and incident light angle (the angle of ray arrival from the strongest source), and two situational sensing modalities: attitude and temperature. The device demonstrated significantly faster response time (> 6x) and a much wider dynamic range (> 10x) in light intensity measurement as compared with the standard MTS310 Mote sensor boards. The light-angle estimation results were well correlated with an average error of just 2.63°. The assembled Illumimote with a lumisphere appears in the picture above. The role of the lumisphere is to protect the sensors and to integrate incident light from all directions.

The overall system architecture diagram of the Illumimote appears below. There are eight light sensor channels allocated based on the number of detector circuits required to capture the illumination attribute. For example, the color temperature unit requires three channels—one for each of red, green, and blue luminosity. Signals from the eight light acquisition units and four situational units are multiplexed via the channel selection unit and presented to the ADC for conversion into a 10-bit digital signal. This resultant data is conveyed to the networked and embedded nodes (in this case, MICAz motes) via either the I2C data bus or a direct 16550Acompatible UART link that uses line-level (rail-to-rail) output. The operation of the Illumimote’s units may be controlled directly from the Mote via the I2C bus or locally by an onboard Atmel Atmega48 microprocessor. Employing the local processor relieves the network interface (mote) of any realtime constraints associated with frame-rate-accurate sampling. The local processor also exposes interrupt facilities both to and from the host-processor onboard the mote. When operating in this mode, the continuous I2C bus may be severed and reattached dynamically (hardware is bus-state aware) to create two isolated buses—one local to the Illumimote, and one local to the Mote—as needed. In addition to calibration functions, the embedded temperature sensor can wake a sleeping mote in the event of a dangerous thermal condition (risk of meltdown). On the bottom, Illumimote features a connector that is compatible with standard Mote-type sensor nodes (IRIS, MICA2, MICAz, Cricket etc).



Three embedded software components were developed for the experimental wireless sensing system. First, sensor and sensitivity control software was programmed and downloaded to the Illumimote board. The board was then attached to a MicaZ node that has a 7.37MHz 8-bit microprocessor and a 250kbps ZigBee radio. Secondly, the Illumimote driver and light sensing application were programmed at the MicaZ mote using SOS environment. SOS is an OS for Mote-class wireless sensor networks developed by NESL at UCLA. Finally, at the base station laptop, a Java program was used to monitor and log the light measurements, and a visualization interface was used for real-time debugging and analysis. A GUI visualization interface was developed as shown below to display the status of the Illumimote in real time, that was used for testing, experimenting, and performing demonstrations. The interface was implemented in Java and Processing. This GUI made it easy to test and evaluate the Illumimotes visually and is a step towards designing the interface that could be used by a cinematographer in future.



The Illumimote achieves performance comparable to a commercial light meter and color meter (as used by professional cinematographers) over the ranges tested. It consists of incident light intensity, RGB intensity (for color temperature calculation capability), and incident light angle sensors as well as thermal and attitudinal sensors. Researchers at UCLA characterized its performance and verified its capabilities. The project website hosts the technical data and the Illumimote will soon be commercially available from Atla Labs to allow other researchers access to the technology for their own experimentation. Future work includes further enhancements to the general characteristics of the Illumimote (such as dynamic range), estimation of the vertical incident light angle, and further development of the software tools that support and integrate the Illumimote in support of its deployment on actual productions scheduled for the near future.

Intelligent Transportation Systems

When I think about the future of transportation, the image that pops into my mind is the world of the Jetsons - a world of automation and intelligent transportation systems. A place where machines have the ability to sense and understand their surroundings to allow for a safer more efficient transportation environment. With major innovations slowly taking place all around us, we do not realize how quickly Intelligent Transportation Systems (ITS) are becoming part of our every day life. Implementations such as FastTrack to stop delays at bridge tolls, new technology in vehicles such as the Lexus LS 460 with advanced parking guidance so the car can park itself, Land Rover with adjustable suspension TerrainResponse™, or the Infiniti with wireless connectivity...all these innovations lend themselves to creating more intelligence in the transportation sector.

What is an intelligent transportation system? The term refers to efforts to add information and communications technology to transport infrastructure and vehicles in an effort to manage factors that typically are at odds with each other, such as vehicles, loads, and routes to improve safety and reduce vehicle wear, transportation times and fuel consumption. The last few years have seen the emergence of many new technologies that can potentially have major impacts on Intelligent Transportation Systems. A recent study by the UK Governments Office of Science and Innovation, which examined how future intelligent infrastructure would evolve to support transportation over the next 50 years looked at a range of new technologies, systems and services that may emerge over that period. One key class of technology that was identified as having a significant role in delivering future intelligence to the transport sector were wireless sensor networks (Motes) and in particular the fusion of fixed and mobile networks to help deliver a safe, sustainable and robust future transport system based on the better collection of data, its processing and dissemination and the intelligent use of the data in a fully connected environment. Motes can also be augmented with additional sensors – such as those for detecting light, temperature and acceleration – hence enhancing their features and making their application areas virtually limitless. It is generally perceived that Motes will become the low-cost, ubiquitous sensor of the future, especially once its size shrinks dramatically to merit its name.

Researchers at Newcastle University have been at the forefront of looking into the technology challenges of using these small, low-cost and smart wireless sensors in transport and the application areas where they could be employed. It is clear to the ITS community that the emergence of low cost sensors will open up new paradigms in how we can pervasively collect data from sensors, convey information along fixed and mobile low cost wireless networks (partly or fully formed or ad-hoc) and provide a ‘connected environment’ where individuals, vehicles and infrastructure can co-exist and cooperate, thus delivering more knowledge about the transport environment, the state of the network and who indeed is traveling or wishes to travel. This may offer benefits in terms of real-time management, optimization of transport systems, intelligent design and the use of such systems for innovative road charging and possibly carbon trading schemes as well as through the Cooperative Vehicle and Highway Systems for safety and control applications. See the research and potential use of a Mote based wireless sensor network in the video below:

Initial studies suggest vehicle to vehicle, vehicle to infrastructure, and infrastructure to infrastructure communication and in-vehicle monitoring and environmental monitoring may exist for Motes in the transport domain. Over the last few years, many different versions of 'smartdust devices' have been designed and built by various companies and institutions. Such devices can be used to sense a wide range of environmental parameters as well as vehicle speed, vehicle direction and vehicle presence in the infrastructure. Even though there are several platforms available on the market, Newcastle University chose Crossbow's MICA family motes for the EMMA and TRACKSS projects due to its commercial success in many wireless sensor network applications. Also, Newcastle University has successfully used MICA family motes in its other research projects such as the ASTRA project. Low power wireless communication and low power sensing capabilities are essential for sensor network applications which are supported by Crossbow's Mote family.

The ASTRA project investigated the use of mobile ad-hoc networks, and more specifically, Motes for transport applications. The project examined the current state-of-the-art using MICA motes. A trial using Motes technology was hosted in Newcastle with a pervasive intelligent corridor established by a network of fixed Motes on roads near Newcastle Central Station. Mobile Motes were also placed in several buses. Communication between a static node and a moving node on-board a vehicle was achieved, showing that communication can take place between road side and vehicles using a network of Motes. Evaluation of the system revealed that the main limitation of the technology at the present time is battery life. The experiments have demonstrated that Motes can be used for communication between a fixed infrastructure and a moving vehicle up to a speed of 50 mph. Further testing of the devices at higher speeds (60 and 70 mph) to asses the suitability of smartdust/Motes in applications alongside fast moving roads such as a motorway will be conducted.

The focus of the EU funded TRACKSS project is to research advanced communications concepts, open inter operable and scalable system architectures that allow easy upgrading, advanced sensor infrastructure, dependable software, robust positioning technologies and their integration into intelligent co-operative systems to support a range of core functions in the areas of road and vehicle safety and traffic management and control. The overall aim is to develop new systems for cooperative sensing and predict flows, infrastructure and environmental conditions surrounding traffic, with a view to improving road transport safety and efficiency. To support the demonstration phase of the project, Newcastle University will develop a new technology for ‘smart’ detection on vehicles and infrastructure and a common framework for data collection and access from the entire array of sensors being deployed and tested in the TRACKSS project.

In the case of EMMA, the focus is automobiles and their constituent parts, and the infrastructure they utilize (both physical in the sense of roads and the ICT embedded in them for monitoring and control purposes). If we think more widely at present, most of the world’s computing power is already embedded invisibly into the things around us. The personal computers, music players and other gadgets are just the tip of the iceberg. They probably represent no more than 1% of the computing power we have deployed around us. A typical car today will have at least 20 microprocessors and a host of other electronics contributing to the general functionality required by a modern car as well as the ‘value added services’ which may be the unique selling point of a particular vehicle – whether the application, be: better information on how the vehicle is running; safety applications; or infotainment in the vehicle. The Embedded Middleware in Mobility Applications project (EMMA) application domain of transport will be taken as a pilot example where EMMA will foster cost-efficient ambient intelligence systems with optimal performance, high confidence and faster deployment. The MICAz Mote will be the best suitable platform for the EMMA project since it features sensing and networking capabilities with low power consumption.

The EMMA and TRACKSS projects being pursued by Newcastle University are committed to play a major role in creating new possibilities in the future ITS by using Mote technology. For more information on Crossbow's Mote platforms, contact sales or visit our website.

Heated up about your Energy Bill? Motes to the Rescue!

by Ralph Kling, Chief Architect, Crossbow Technology, Inc.

When I opened up my Gas and Electric bill from our local utility (PG&E here in Northern California) I was shocked: it was over $100 more than I expected. Some further checking confirmed that it was also substantially higher than during the same time last year, by about that amount. So what happened? Sure, energy prices likely have increased since last year and maybe it was a bit colder here the last month. But it still didn’t add up… 

A few years ago I had installed a shiny new programmable digital thermostat to replace an old mechanical gizmo that must have been around since the house was built in the 1950s. And I actually did read the manual (well, some of it anyways) and spent quite a bit of time programming it with different cycles for days and nights and weekends and so on. And, though I was very proud of that accomplishment at the time, the promised energy savings that I had hoped for actually never really materialized. My utility bills stayed pretty much the same before and after the thermostat installation. 

So after receiving the last bill, I investigated the thermostat but it seemed to work just fine and none of the settings had been changed since I had originally made them. After contemplating this for a bit, I had an idea. Since I am working at Crossbow, the leader in Wireless Sensing technologies I have access to the essential tools needed to solve this mystery: I took home a Wireless Sensor Starter Kit which consists of two sensor nodes and a base station. I also added a Stargate Netbridge gateway to record and visualize the data from the sensor nodes without the need to install anything on my PC (or even have it running and consuming energy during the measurements). 

The next day, I installed the “Sensor Network” in my house. It was really simple, just plug the gateway into my home router, place the sensor nodes in the rooms I wanted to monitor and turn them on. All networking and data gathering happen automatically from then on. The sensor nodes are battery powered thus they can be placed pretty much anywhere eliminating the search for outlets. I placed the nodes in the kids’ rooms in the center and back of the house. The nodes have all sorts of sensors including light, humidity, atmospheric pressure etc. but I was particularly interested in the temperature readings. 

The next day, I pulled up the temperature profile gathered so far and I was shocked:

 

The Thermostat was set to 70°F, (about 21°C) during the morning and evening and to 65°F, (18°C) during the rest of the day and at night. The measurements show an average of 24°C during the high and 20°C during the low period, a full 2-3 degrees more than expected. Why were the readings so far off from the preset values at the thermostat? And then it dawned on me: the thermostat was in the living room at the other end of the house. And I remembered that last year in the summer I had closed the heating air vents in that room to prevent the kids’ Lego pieces from falling into the shafts (they are really hard to retrieve from there). Of course I had forgotten about that in the fall when I restarted the heater. 

So the warm air had to make its way through half the house before the living room (and the thermostat) warmed up thus raising the temperature in the other rooms way to high (as well as the heating bill). But this was still a theory that needed to be proven. So I opened up the living room vents all the way, let the sensors collect more data and waited impatiently for the data from the next day. The results were nothing short of astounding:

 

The temperatures in the kids’ rooms now averaged 21°C during the high and 19°C during the low periods, much closer to the desired thermostat settings. Beyond that, the data also shows that one room warms up more poorly than the other (it has more outside walls with poor insulation). Adjusting the vents in those rooms as well could further equalize the temperature readings. 

So, with a very simple setup (it took literally 5 minutes to set up) I was able to gather a lot of valuable data and hopefully significantly reduce my heating bill (we will see next month). And all that without lowering the temperature settings on the thermostat! Beyond that there are lots more insights that can be gathered from the data: The slope between the day and night settings shows how fast the house cools down and how well it is insulated. Other data like the humidity measurements can point to potential problems with mold if the readings are too high. Do your kids always “forget” to turn off the lights – well a plot of the data can very convincingly show how much energy is wasted. 

I am sure there are lots more good ideas – reader’s suggestions are welcome!

Dr. Ralph Kling is currently the Chief Architect of the Wireless Business Unit at Crossbow Technology. Ralph is leading the Wireless engineering team at Crossbow and is responsible for new product strategies, technical directions and Standards activities.

Previously, Ralph was Principal Architect and Director of Sensor Network Operation at Intel Corporate Research. In this capacity, he was responsible for ground-breaking research in the area of Wireless Sensor Network Platforms that resulted in such novel designs as the Intel Mote and Imote2. Before joining Intel Research, Ralph's previous assignments include managing the Itanium® Processor Family microarchitecture/ performance group and the Microprocessor Research Lab (MRL).

Ralph obtained his Master's and Ph.D. degrees from the University of Illinois at Urbana-Champaign. His thesis research focused on Simulated Evolution, a new global optimization method for integrated circuit designs. Prior to coming to the US on a Fulbright scholarship, Ralph studied Electrical Engineering in his hometown of Hanover in Germany. He enjoys skiing in the winter and beaches in the summer.

Motes in Antarctica!

Can you imagine a place where the temperature can get to -82°C? A place without sunlight for half the year. Imagine a frozen desert with no permanent human population - the coldest, driest and windiest continent on earth... It is in this place that research scientists from the Institute of Remote Sensing Applications at the Chinese Academy of Sciences have deployed a wireless sensing system using Crossbow's Mote platforms. Being a resident of sunny California I can barely fathom such an environment. If the temperature drops below 50°F (10°C), I want a cup of hot chocolate and a fire to cuddle in front of. Imagine being in a place where the climate is so harsh - there are no plants or animals. Talk about extreme sensing!

China News reported that the wireless sensor network called the 'Unmanned Wireless Intelligent Snow and Ice Observation System in Extreme Environments' has been successfully set up around the Dome-A area near the South Pole, which is the southernmost point on Earth's surface. Dome-A (Dome Argus) is the highest and possibly coldest place in Antarctica and perhaps the coldest naturally occurring place on Earth. It is the highest ice feature in Antarctica, comprising a dome or eminence of 4.093 m elevation.  The system was co-developed by Crossbow Technology and the Chinese Academy of Science to develop a solution that can consistently work under extreme environmental conditions such as a 4-month polar-night, -82°C temperature lows and an annual average temperature of -55°C. The wireless system deployed by CAS scientists is designed to overcome the low-temperature, high-altitude and soft snow-surface.

The deployed system consists of two base stations and four nodes. Each node is powered by two low-temperature resistant batteries which are expected to last for one year. The communication capability between each node can achieve ranges of up to 1000m. Each node samples environmental data every 15 minutes, including temperature (weather temperature, snow temperature and the snow temperature below 1 meter), humidity, sunlight, and air pressure. The collected data is sent wirelessly to the central base station that collects and sends the data to Beijing every day. Meanwhile, the other remote base station is used to store the data locally to be collected later by an expedition team. The two base stations ensure that no data is lost in the communication.

The Antarctic ice sheet where Dr. Xiao and his scientific expedition team ventured is the highest point on the continent. The group hopes to gather vital information on the environment and observing conditions around Dome-A to determine whether or not it is a viable location to expand the observatories in the region. Dome A claims the best astronomical sky conditions in the world, as it is devoid of clouds and boasting steady air that makes for clear viewing. Imagine...Motes in Antarctica!

Bacterium-inspired Robots for Environmental Monitoring

Bacteria - what comes to mind when you hear that term? I think of something that spreads quickly and takes its "message" with it wherever it goes. Bacteria are ubiquitous in every habitat on Earth. The idea of relating something as universal as bacteria to motes is interesting as the design of wireless sensor networks was to make pervasive computing a reality where any environment could be smart. It is estimated that there are approximately five nonillion (5×10³⁰) bacteria in the world. In a world as connected as ours, the notion of having motes as pervasive as bacteria is becoming a reality.

Researchers at the Center for Robotics and Embedded Systems at the University of Southern California took this concept to reality by developing Bacterium-inspired Robots for Environmental Monitoring. Using the behavior of bacteria to inspire their research on the mote platform, an interesting application for motes was developed. Locating gradient sources and tracking them over time has important applications to environmental monitoring and studies of the ecosystem. The approach taken at CRES was to imitate bacterial chemotaxis. Chemotaxis is defined as the phenomenon in which bodily cells, bacteria, and other single-cell or multicellular organisms direct their movements according to certain chemicals in their environment. This inspiration is reflected in the algorithms and robots designed which navigate to sources using gradient measurements and a simple actuation strategy.

Several phenomena in nature induce gradients in their environment. For example, a fire induces a temperature gradient in its vicinity; an oil spill induces a concentration gradient of oil in water, etc. The ability to autonomously detect, locate and track such phenomena would give scientists a tool to monitor and study ecosystems at an unprecedented level of detail. Typcial gradients of interest to scientists studying the ecosystem include temperature, light, salinity, mineral concentration, pH, etc. These problems are difficult because of the time-varying nature of the source, the dynamics of the environment, a multiplicity of sources and a paucity of sensing. The focus at CRES was to develop a simple mobile robot that would navigate to such a source using gradient information and extremely rudimentary actuation - a strategy inspired by the study of chemotaxis in bacteria.

The novel technique introduced was based on random walk for the detection, seeking and tracking of gradient inducing source phenomena. The ability of a sensor node to move itself or to otherwise influence its location could be critical for certain sensor networks. The ability to combine computation, sensing, communication and actuation to not only passively monitor the environment but also actively track, and in some cases mitigate problems is the design that Robomote was hoping to represent. Bacteria sense chemical concentration using receptors. They produce motion using their flagellum and the duration of their action is related to the concentration gradient. Based on this description of bacterial motion it was determined that a strategy based on biased random walk can be used to located and track gradient sources with the simple Robomote.

In this way, the MICA Motes from Crossbow that the Robomotes were built upon provided control commands to the device for performing a biased random walk. A basic sensor board with a photo sensor was mounted on the Robomote and a light source was placed at one end of the test bed to generate a photo (light) gradient. The test showed that the Robomote was able to locate the light source by taking samples with the photo sensor to determine whether it was heading in the right direction making turns as necessary. The Robomote was able to make decisions even when given multiple light sources to determine how to reach a source. Through the simulation and experimental work on robots, the researchers at CRES were able to demonstrate how their strategy of using the bacteria inspired response of chemotaxis in motes is well suited to the varied conditions multiple sources, dissipative sources, noisy sensors and actuators create. The approach was validated by testing it on the Robomote in a phototaxis experiment. The algorithem developed is scalable and ideal for implementation on simple low-cost robots. The day that Motes and pervasive wireless sensor networks will be as ubiquitous as bacteria may not be too far away!

Intelligent Wireless Asset Tracking of Packaged Gases

Asset tracking is basically defined as a system that enables one to track an asset using several technologies. Asset tracking is an essential part of many industries, in particular, those concerned with logistics, purchasing and manufacturing.

Gas cylinders are used in many different situations, such as in research, in industry, in healthcare, and even in the home. They are tanks or pressure vessels used to store gases at high pressure. The transportation and storage of gas cylinders is regulated by most governments. Due to the demand  for monitoring and tracking in such a wide variety of circumstances, there is an inevitable ambition of gas suppliers to improve the efficiency of their business. A prototype system developed at Liverpool John Moores University addresses a new idea to use Motes to provide a tracking system that would improve efficiency while also integrating sensors in order to monitor gas cylinders from a safety perspective.

The aim of the prototype system was to discover whether or not communication between motes was possible and reasonably reliable despite signal attenuation which would be caused by the metallic surroundings. This simulated asset tracking environment contained caged gas cylinders which were stored outdoors. Each of the cyclers used during testing has a MICA2 Mote securely attached to its collar via a nylon cable tie with each mote arranged randomly to create non-line of sight (NLOS) conditions. The antennas were in various orientations and touching the metal gas cylinders. The situation was setup as a worst case scenario for RF communications in this application.

The base gateway received the data from the network and would communicate the information to the computer which had stored the network addresses or node IDs of the motes which were arbitrarily associated with a particular type of gas (e.g. Nitrogen, Oxygen, etc.). In order to ensure reliable communication between the base station and the motes attached to the gas cylinders outside, an intermediate relay node was used - it was placed outdoors with a brick walled building between. The prototype system was successful in collecting data within such an environment. Users were able to determine which tags were active at any one time, communicate with individual tags via their unique node ID in order to trigger an event such as sounding the buzzer on board. This two-way communication allowed tags to be identified and provide sensory data.

This idea for an automated gas storage facility is to monitor the gas cylinders when they are in motion. The facility could be unmanned and treated as a location where the gas cylinders were picked up and dropped off. When entering the facility, motes could be interrogated to identify themselves (i.e. their contents, from where they were being returned, etc.). Each mote would not need to rely on a specific reader to send its data, but could communicate the data over the multi-hop mesh network. This would allow data on what the cylinders needed to be filled with, when, or for who to be sent quickly and efficiently to the warehouse. This type of data would prove advantageous to the end users as well to automatically trigger an order of new stock of the particular gas that had been used, etc. Primarily, It also helps with safety issues if interfacing some type of pressure sensor to monitor the contents and prevent a dangerous or explosive situation. Accelerometers could be used to determine the orientation of the cylinder and worn the user if it is stored incorrectly. The flexibility of the mote platform to interface with various sensors is greatly useful for such an application.

This capability to monitor and track assets intelligently will enable a more reliable and safe environment especially for critical applications such as the storage and transportation of packaged gases!   

World's Largest Agricultural Wireless Sensor Network

About one month ago, the Department of Primary Industries (DPI) issued a statement that they have deployed the world's largest agricultural wireless sensor network. DPI has taken a pro-active approach to the maintenance and building of Victoria's scientific capability and is committed to the provision of high quality, innovative science and technology to create robust primary industries. A project was launched in July 2006 that is to conclude in June 2008 to evaluate sensor network systems and address a variety of environmental and agricultural issues. The area being tested was a nectarine orchard covered with 273 sensors supported by Crossbow's MoteWorks software platform.

Researchers focused on developing Integrated Smart Sensing Systems to develop the capability of DPI to use wireless sensing and microtechnology to improve the agricultural and environmental outcome in  Victoria. The Orchard study is designed to build capability in a long term, spatially dense sensing environment in which a production system operates and its performance is monitored. Led by the plant production sciences platform staff, the field study examined the effect variability in soil moisture over a growing season on the variability in canopy cover and fruit yield.

The major ISSS capability was to be developed by deploying a WSN which would collect soil moisture measurements at three soil depths, at up to 100 locations, each hour for the duration of the study. Additional sensor information was to be gathered by deployment of more sophisticated sensors in smaller numbers. Measurements of the soil structure, canopy density, local climate, irrigation activity and fruit yield were to be made using conventional techniques and would provide supplementary data on the environment and the product of this horticultural system.

The sensor system used for the orchard deployment consisted of a gateway connected to the internet gathering data from the 433MHz MICA2 Mote platform. The data was collected from the 'weather chip' consisting of temperature, humidity, light, wind speed, wind direction, leaf witness and soil moisture sensors. The initial measures of soil variability and tree canopy would aid in increasing the positive output of the production measures in fruit yield and irrigation usage. Daily network health statistic alerts are sent via SMS to a mobile phone. 

Crossbow’s unique wireless networking solution is an industry first in its ability to integrate the latest wireless mesh technology to collect practical environmental data Air Temperature, Relative Humidity, Location (GPS), Ambient Light, Solar Radiation, Barometric Pressure, Precipitation, Soil Moisture/Temperature, etc. Mesh sensor technology allows scalable range extension by node to node sensor communication. This technology has already been proven in numerous applications and is ideal for deployments where multiple sensor nodes are required. The importance of monitoring our physical environment has never been higher. Many groups from agricultural operators to natural resource developers to biological researchers to homeland security, all need to make reliable, sensitive measurements in remote or dispersed locations. Crossbow's XMesh based wireless solutions and low-cost MEMS based sensor capabilities enable breakthrough environmental monitoring performance for our customers.

Hybrid Wireless Sensor Network for Cane-Toad Monitoring

Crossbow's Mote products have been used for so many diverse and interesting applications. One of the most interesting deployments was done in 2005 using a wireless system to monitor cane toads. You may wonder why anyone would want to monitor toads, so here is some background on this little problem.

The amphibious assault and invasion of the cane toad began in 1935 when the toad (native to South America) was introduced to the sugar cane fields of Queensland in Northern Australia to eat a beetle that was damaging the state's sugar-cane plantations. The experiment was a failure as the cane toads ignored the beetles and began chomping their way through other wildlife from frogs and tadpoles to small lizards. Even worse, the poisonous glands on their backs made them deadly to the crocodiles, mammals, snakes and birds that tried to eat them (a mouthful of toad could be fatal to a dog or cat). Even the tadpoles were poisonous to native animals. The toads adapted quickly to the heat and humidity of tropical Queensland and within decades moved South and West, and finally overran the world famous Kakadu National Park - a World Heritage site. While the region of Darwin was already besieged, millions of other toads were converging on Western Australia. Females could lay up to 35,000 eggs at a time making efforts to head them off difficult. The federal government looked into cane toad control research through its scientific agency, the Commonwealth Scientific and Industrial Research Organization (CSIRO) to find pathogens or other agents to wipe out the pests.

The toads can not be rounded up like cattle, but must be caught one by one - it is only through volunteer efforts that Australians can stop this amphibian invasion. To aid these efforts, a collaboration between the University of New South Wales, Portland State University and National ICT Australia aimed to design a wireless sensor network that could work unattended. In past deployments, researchers from UNSW used PDA class, disconnected devices. The prototype developed in this project provided a cheap and scalable alternative using networked sensor motes. The wireless acoustic sensor network used automatic recognition of animal vocalizations to census the populations of native frogs and the invasive toads. The wireless sensor network was designed to recognize vocalizations of up to 9 frog species found in northern Australia. The system used Crossbow's Stargate platform and MICA2 Mote products. The MICA2 Motes were used to collect acoustic samples and expand the sensor network coverage while the Stargate were used for resource-intensive tasks such as FFTs and machine learning.

The development of the frog vocalization algorithm provided a key feature for the deployment. Acoustic features in the time and frequency domains could be used to distinguish the vocalizations of different amphibians. Frog vocalizations are much simpler than human speech, but they must be recognized in very difficult conditions (wind, rain, insects, and other prevalent noise). The algorithm examines each slice of the spectogram to extract attributes of the individual species being targeted by using pre-set classifiers. The use of this algorithm with the wireless sensor network was to pinpoint the regions inhabited by cane toads and to track their macro movement directions as the system was deployed in boundary regions. Researchers used a hybrid network of Stargate and MICA2 Motes to make the system cost-effective. The MICA2 Motes were scattered to collect acoustic samples while the resource-rich Stargate platform was used to run the FFT algorithm and other functions required. The MICA2 Mote platform performed preliminary processing on the samples to reduce the transmission size and environmental noise of the data sent to the Stargate. The Stargate would then use these inputs to determine the existence of frogs and pinpoint their location. The macro movements were estimated by comparing the cane-toad existence snap shots at different times by using the location of the sensor device that detected the existence of the species through vocalization. This location information proved to be more than adequate for tracking the cane toad's long-term migration patterns. Although the system would sometimes confuse species, it would never give incorrect results for the cane toad species (the principal species being detected) since it has a very different vocalization compared to the other species found. This type of detection helped volunteers determine which areas they needed to target, and which areas could be left alone allowing for workers to be used more efficiently rather than spending time doing broad sweeps of the area in search of the toad.

Deployments such as these show the vast capabilities and applications into which the Mote platforms may be deployed. Their flexibility and ability to provide a low-cost solution for remote sensing applications presents situations where technology can enable us to understand and monitor our physical environment in greater detail.