Crossbow Technology in M2M Top 100 for Fourth Consecutive Year

The 2008 M2M 100 is a list of the most important and influential machine-to-machine technology providers as determined by the editors of M2M magazine and its editorial advisory board. First published in 2005, when Crossbow was first listed, the M2M 100 list is widely regarded as the definitive record of companies representing the fast emerging machine-to-machine technology market.

The list is compiled from over 300 companies and the Top 100 are selected after extensive research and analysis that takes into consideration the strength of their business model, product portfolio, growth potential, customer references, market leadership and a variety of other factors. It is designed to provide a snapshot of the market as it exists today and the companies with the greatest impact on its direction. Many companies were considered for this year's directory, but only the M2M 100 passed the rigorous selection process. The list is published annually.

Crossbow has been listed in M2M's Top 100 since the list's conception in 2005. In all 4 years, Crossbow has maintained its position as the market leader in the wireless sensor network space. With its innovative new products and a proactive focus on providing customers with the latest in technology and capability, Crossbow offers users a full portfolio of hardware and software solutions with strong customer support. Crossbow's strategic relationships (i.e. collaboration with Microsoft to develop the .Net MicroFramework for the Imote2, National Instrument's LabView development for the Mote sensor platforms, etc...) gives the company the ability to provide a broader scope for wireless sensor network technology.

Crossbow has shipped over 500,000 sensors to over 1000 customers, including select Fortune 100 companies for diverse applications such as industrial automation, building monitoring, home automation, environmental control, defense, security and asset tracking. Crossbow changes the technology landscape with leading-edge inertial systems and is at the heart of innovative wireless sensor networks with low-power, open architecture platforms. We are leading the revolution for connecting the physical world with the digital world through wireless sensor networks.

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!