Wireless Body Sensor Networks

Recognizing human activities using Wireless Body Sensor Networks (WBSNs) has received considerable attention from both academic researchers and industry practitioners in the past few years. This task is particularly challenging because human activities are performed in not only a simple (i.e., sequential), but also a complex (i.e., interleaved and concurrent) manner in real life, and often involved in multiple users.

At the University of Southern Denmark, there are several research projects, led by Dr. Gu, investigating the fundamental problems of recognizing real-world, complex activities for both single and multiple users in WBSNs. They have designed a multimodal WBSN to capture observations for multiple users. It consists of a number of Imote2s, two RFID reader Motes, and an acoustic sensor node as shown in the figure above. An Imote2 node captures movement, environmental temperature, humidity and light. An RFID reader Mote captures object use within several centimeters, and it consists of a MICA2DOT Mote and a coin-size short-range RFID reader. It also uses an audio recorder as an acoustic sensor to capture sound.

They then conducted data collection by two subjects in a home environment. Each subject wore a set of wearable sensors and performed various activities by following his/her own step for each activity. Some snapshots are shown in the figures below.

To recognize both single- and multi-user activities, they propose a novel pattern based approach.  Both activity models leverage on a discriminative knowledge pattern that describes significant differences between two classes of data. In addition, the multi-user model is able to capture user interactions. The pattern-based approach has the following advantages. First, the models for complex activities (i.e., interleaved and concurrent) can be built directly from the model for sequential activity without the training process. Second, since a discriminative pattern captures significant differences, random noise can be easily eliminated. Third, the pattern-based approach offers better scalability as compared to traditional machine learning approaches. With the above characteristics, they are moving a big step towards real-life activity recognition systems. Their comprehensive experimental results demonstrate that their approach outperforms the state-of-the-art schemes in terms of accuracy, applicability, scalability and robustness.

In addition, they also develop a hierarchical model for real-time activity recognition which first identifies simple gestures at a sensor node level, and then recognizes activities at the portable node level. There are several conference and journals publications out from these projects. The dataset and code are also available for research purpose. You may refer to this link at http://www.imada.sdu.dk/~gu for more information. 

 

ASSERT Yourself!

Someone once said, "To know oneself, one should assert onself." Researchers at the University of Texas at Dallas Distributed Systems Lab, took this notion and created an Advanced wireleSS Environment Research Testbed. ASSERT provides users with the true ability to know oneself. In many instances, simulation allows for rapid development and testing, but it does not provide a realistic RF environment. To compensate for this field experiments are performed yet when problems are encountered, it can be difficult to located and correct them. ASSERT provides users with the advantage of simulations and field experiments without the suffering and disadvantages of both! The goal of this testbed is to provide experimenter with the ability to run controlled, repeatable experiments that would have a similar performance to that of field experiments without the associated deployment and travel costs.

ASSERT has been a work in progress for the past two years and currently offers a forty site testbed. As wireless networking is becoming more pervasive there has been a greater desire to develop communication hardware and protocol stacks that have a number of desirable properties like increased throughput, reduced latency, reduced energy consumption, quality of service security, etc. ASSERT is a true emulation testbed. Field testing requires extensive time and energy. Changes to the location of devices require physical movement of the equipment and measurement devices from place to place making repeatability a difficult task. 

The hardware for each site consists of two mated boards, the Site board and the RF board. Each Site board consists of an ARM 200MHz processor, 256MB of on board RAM, a Xilinx FPGA, support for an external clock and various peripheries such as Ethernet and RS-232. The RF board consists of 17 SMA coaxial connectors, 16 of which are used to connect to other Site-RF board pairs and 1 dedicated to the Unit Under Test (UUT). The current system integrates the Crossbow/MEMSIC MICA2 (900MHz) Mote platform with gateways as UUTs, however any device operating in the 700MHz to 1100MHz range is natively supported. 

ASSERT emulates the distance between multiple devices by using variable attenuation - this means that identical tests can be performed in the lab compared to the field. ASSERT also allows users to conduct experiments in licensed frequencies like the cellular service bands without interfering with the services offered by the owners of these licensed bands. By changing signal attenuation levels, using programmable attenuators and amplifiers on the path between nodes, UT researchers emulated the formation/disruption of wireless links and node mobility. Using power dividers/ combiners they are able to emulate complex, multi-hop wireless sensor networks. As all communication is over coaxial cables, the network is immune to interference from other wireless networks, or allows the flexibility for users to inject the desired amount of noise into the links.

The ASSERT software performs a variety of tasks including monitoring for faults, efficiently allocating resources and providing an easy to use interface. ASSERT currently consists of forty nodes and is designed to scale to at least one thousand nodes without any design changes. Researchers at UT Dallas are converting the UI from the current small Java program to a web based application so that the experiment set up and data gathering is done by the experimenter from their browser. Through the sophisticated custom hardware and easy-to-use control software ASSERT has many valuable features that allow it to reduce the cost of testing wireless networking protocols at scale. This device independent platform for developing and testing wireless network protocols has taken experimentation to a new level!

For more information on the MICA2, click here.
For more information on ASSERT, click here.

The Business Value of Wireless Sensor Networks

Crossbow's advancements in the field of wireless sensor networks was recently featured in the current issue of M2M Magazine. The article deals with the business value of wireless sensor networks and how information derived from WSNs have a powerful effect on the way companies are making decisions in today's world. Here is an excerpt from the article:

A WSN consists of spatially distributed autonomous devices using sensors to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion, or pollutants at different locations. Each node in a sensor network is typically equipped with a radio transceiver or other wireless communications device, a small microcontroller, and an energy source, usually a battery. The size of a sensor node can vary from shoebox-size to the size of a grain of dust.

Even in this economy, companies are finding it easy to make the case for investing in sensor networking. Shana Farley, Marketing Manager, Crossbow Technologies, www.xbow.com, San Jose, Calif., concurs, saying, “Companies will see significant ROI on their WSN investments because the data they are collecting and analyzing is usually in the interest of saving money on consumption, resources, manpower, equipment, etc.”

Crossbow Technology has seen its solution used in typical scenarios such as predictive maintenance on machinery to more unique applications, such as monitoring endangered species, measuring the force of a running horse at the Kentucky Derby, and determining drought conditions in the Sierra Nevada mountain range.

The company’s eKo Pro Series was developed for environmental monitoring. At least four sensors can be attached to one eKo Node. Once customized to a client’s specific monitoring needs, the eKo Nodes form a wireless sensor network. A base radio with standard antenna serves as a collection point, receiving data from the deployed eKo Nodes. Field data is then transmitted to the plug-and-play eKo Gateway, a self-contained embedded Linux-based Web server device. The Gateway is pre-installed with eKoView, a secure Web interface, for viewing real-time data, running reports, and setting up alerts.

For the data to be useful, a reaction must be triggered. “It might be that a perimeter intrusion automatically turns on a camera or that a sprinkler is turned off when a moisture threshold is achieved in a crop field,” says Farley.

Wireless sensor networks are opening up a new world of monitoring, just as wireless phones did for the telecommunications industry. They are enabling companies to transform their businesses. “The main idea behind WSN is that information that was so difficult to collect in the past is so easy to get today,” says Farley. “You can collect data and make smarter decisions. You do not need wires or a person collecting data 24/7. The idea is to connect the physical world with the digital world through wireless sensor networks.”

To view the full article, visit the M2M site here or for more information on Crossbow's wireless sensor networking products, click here. M2M refers to machine-to-machine communications through remote monitoring, RFID, sensor networking, smart services, telematics, and telemetry.

Wireless Sensor Networks Under Fire

Researchers at the University of Corsica off the coast of France released information regarding development efforts they have been conducting dealing with using wireless sensor networks as a reliable solution for capturing the kinematics of a fire front spreading over a fuel bed. This work was financed by the Territorial Community of Corsica to provide reliable information in fire studies and support fire fighting strategies. To do this a wireless sensor network must be able to perform three sequential actions: 1) sensing thermal data in the open as the gas temperature; 2) detecting a fire i.e., the spatial position of a flame; and 3) tracking the fire spread during its spatial and temporal evolution.

Researchers at University of Corsica took on this challenge as the Mediterranean area is a region experiencing strong ecological variations due to global climate change. Wildland and forest fires represent an important cause of natural hazards and disasters for assets and human lives every year. Early detection and accurate tracking of the fire front propagation are key points in fire fighting strategies to minimize the damage and possible casualties. According to fire managers, the delay in detecting a fire ignition in the open is considered as one of the main factors increasing the response time of the first fire fighting actions. The resulting size of the fire to be managed as the final burning area strongly depends on this response time of the first fire fighting action.

One of the great and obvious challenges in performing fire front tracking with WSNs is to avoid the destruction of the Motes by the fire. This effort shows the ability and performance of a Wireless Sensor Network, when the Motes are protected with a thermal insulation dedicated to track a fire spreading across vegetative fuels on a field scale. The resulting experimental WSN was then used in series of wildfire experiments performed in the open in vegetation areas ranging in size from 50 to 1,000 m².

Using Crossbow's MICA motes and MTS420 GPS enabled sensors, researchers designed the Firesensorsock. The goal of this protection was to dampen the thermal impact from the fire on the Motes during the fire spread and contact. This shield also had to allow both a continuous emission of data and the temperature and the hygrometry inside the sock to vary on short time scales for locating the fire’s position. This innovative technique packaged the Mote and sensors to be thermally protected, preventing their destruction and extending their lifetime beyond the fire event. With this thermal insulation, the network could be reused for subsequent events. The key was to provide a protection able to preserve continuous flow of data from sensors embedded in a thermally destructive environment, namely a large scale natural fire. When sensors were covered with Firesensorsock the temperature and humidity measured during fire experiments was relative to what was measured inside the protection. The Firesensorsock consists of several layers of thermal insulation materials: a layer of simple Zetex fiber, a layer of ceramic wool and a final layer of aluminized Zetex fiber. These layers are fixed to one another with Kevlar thread. This level of protection allowed the sensor to support prolonged fire contact and also wireless communications without any disturbance. The sensing of any significant change of thermal data in the protection, due to the change of external thermal medium governed by the fire allowed for fire detection and tracking.

The sensors were triggered by the arrival of the fire and enabled the researchers to distinguish exactly the direction of the fire’s spread. The estimation of the rate of fire spread was possible because no node was destroyed by the fire in these experiments. It should be noticed that the computed rate of spread is not the same over the whole grid. This feature is consistent with real fire dynamics: indeed, according to infrared video imaging of the horizontal plot, the fire front propagates with a strong asymmetry. The sensing of the thermal data inside the sock which suddenly varies according to the presence of a strong thermal environment, i.e., a flame, leads to the opportunity to detect the presence of a fire. This point was very important for managing fire alerts in a specific areas because it determined the action of the first fire rescue team.

Because it has not been possible to follow the evolution of a fire with current systems available, this new solution was created -  the double system (sensor + Firesensorsock) provided an adapted tool for natural fire scenarios. Results illustrated that the temperature and humidity variations in the socks allowed users to relevantly determine the presence of a fire and becomes a measurement system for the rate of fire spread in the open. The different tests performed during these fire experiments illustrate the ability of the Firesensorsock system to track the fire's evolution!

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.

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.