Fault Tolerant Maximum Likelihood Event Localization in Sensor Networks Using Binary Data

This paper investigates Wireless Sensor Networks (WSNs) for achieving fault tolerant localization of an event using only binary information from the sensor nodes. In this context, faults occur due to various reasons and are manifested when a node outputs a wrong decision. The main contribution of this paper is to propose the Fault Tolerant Maximum Likelihood (FTML) estimator. FTML is compared against the Centroid (CE) and the classical maximum likelihood (ML) estimators and is shown to be significantly more fault tolerant. Moreover, this paper compares FTML against the SNAP (Subtract on Negative Add on Positive) algorithm and shows that in the presence of faults the two can achieve similar performance; FTML is slightly more accurate while SNAP is computationally less demanding and requires fewer parameters.