Coordinate Formation in Sensor Networks
What	We demonstrate that it is possible to achieve accurate localization and tracking of a target in a randomly placed wireless sensor network composed of inexpensive components of limited accuracy. The crucial enabler for this is a reasonably accurate local coordinate system aligned with the global coordinates. We present an algorithm for creating such a coordinate system without the use of global control, globally accessible beacon signals, or accurate estimates of inter-sensor distances. The coordinate system is robust and automatically adapts to the failure or addition of sensors. Extensive theoretical analysis and simulation results are presented. Two key theoretical results are: there is a critical minimum average neighborhood size of 15 for good accuracy and there is a fundamental limit on the resolution of any coordinate system determined strictly from local communication. Our simulation results show that we can achieve position accuracy to within 20% of the radio range even when there is variation of up to 10% in the signal strength of the radios. The algorithm improves with finer quantizations of inter-sensor distance estimates: with 6 levels of quantization position errors better than 10% achieved. Finally we show how the algorithm gracefully generalizes to target tracking tasks.
Which	IPSN-2002 paper Journal paper
Why	Locations are important for interpreting sensor data.
Who	Radhika Nagpal, Howie Shrobe, Jonathan Bachrach
How	simulations
When	Dec, 2002
Where	MIT AI Lab
And