Subtract on Negative Add on Positive (SNAP) Estimation Algorithm for Sensor Networks

This paper investigates the use of wireless sensor networks for estimating the location of an event that emits a signal that propagates over a large region. In this context we assume that the sensors make binary observations and report the event (positive observations) if the measured signal at their location is above a threshold; otherwise they remain silent (negative observations). Based on the sensor binary beliefs, a likelihood matrix is constructed whose maximum value points to the event location. The main contribution of this work is SNAP (Subtract on Negative Add on Positive), an estimation algorithm that provides an efficient way of constructing the likelihood matrix by simply adding plusmn1 contributions from the sensor nodes depending on their observation state (positive or negative). This simple and efficient estimation procedure provides very accurate results and turns out to be fault tolerant even when a large percentage of the sensor nodes report erroneous observations.