Fault tolerant event localization in sensor networks using binary data

This paper investigates the use of wireless sensor networks for estimating the location of an event that emits a signal which propagates over a large region. In this context, we assume that the sensors make binary observations and report the event if the measured signal at their location is above a threshold; otherwise they remain silent. Based on the sensor binary beliefs we use 4 different estimators to localize the event: CE (centroid estimator), ML (maximum likelihood), SNAP (subtract on negative add on positive) and AP (add on positive). The main contribution of this paper is the fault tolerance analysis of the proposed estimators. Furthermore, the analysis shows that SNAP is the most fault tolerant of all estimators considered.