SToMP

“Receiving the information we need is not the problem­s--sorting it and deciding what is useful without being totally overwhelmed is the primary challenge,” says Robert Ghrist, an associate professor of mathematics and a researcher at the Coordinated Science Laboratory at Illinois. He continues, “Imagine, for example, that you have thousands upon thousands of mobile video cameras and one of them catches something important. What do you do now? And, to make it interesting, let's assume that you don't have GPS, range finders, orientation sensors, or a compass. What now?”

The answer to this “What now?” question is the goal of a new research project called SToMP (short for “Sensor Topology & Minimal Planning”) funded by the Defense Advanced Research Projects Agency (DARPA). This $7.98 million project will cover three phases of development over four years. As a multi-year, interdisciplinary project, SToMP will implement global topological tools to dramatically reduce the amount of sensing complexity needed to solve problems across a variety of Department of Defense applications involving sensor networks, autonomous systems, and configurable sensor platforms.

Public video cameras, RFID-enabled warehouses, and mixture monitors in a chemical plant are all examples of extensive sensor networks that may one day feed enormous amounts of information into self-monitoring systems that will pluck useful kernels from the chaff. In short, SToMP is all about integrating local readings from many sensors into a global picture and bring out important information out of that mass of seemingly random data. One potential application could be to detect holes in the coverage of a cell phone network. Topology mathematics can map the twists and curves of the holes. Once topology has captured sensor information showing where holes exist, it can map them out in a way that provides the guidance needed to fix them. Another such example could be the proper use of a grid of, say, 1,600 motion sensors, each occasionally offering irrelevant feedback such as leaves dropping from trees. In a display of such continuous feedback, a human eye may miss intruders proceeding through a monitored area. But topology algorithms resulting from the research will spot them,

Another feature of the project is its emphasis on minimality. The "minimal planning" part of the project reflects its goal of building the smallest sensor network necessary to get a job done, as opposed to overinvesting in sensor placement.

Institutes involved in project SToMP are University of Illinois, Bell Labs/Lucent, Arizona State University, Rochester University, Carnegie-Mellon University, Melbourne University, the University of Pennsylvania, and the University of Chicago.