Fault-Tolerant Algoritms for Detecting Event Regions in Wireless Sensor Networks Using Statistical Hypothesis Test

Detecting event regions in a monitored environment is a canonical task of wireless sensor networks (WSNs). It is a hard problem because sensor nodes are prone to failures and have scarce energy. In this paper, we seek distributed and localized algorithms for fault-tolerant event region detection. Most existing algorithms only assume that events are spatially correlated, but we argue that events are usually both spatially and temporally correlated. By examining the temporal correlation of sensor measurements, we propose two detection algorithms by applying statistical hypothesis test (SHT). Our analyses show that SHT-based algorithm is more accurate in detecting event regions. Moreover, it is more energy efficient since it gets rid of frequent measurement exchanges. In order to improve the capability of fault recognition, we extend SHT-based algorithm by examining both spatial and temporal correlations of sensor measurements, and our analyses show that extended SHT-based algorithm can recognize almost all faults when sensor network is densely deployed.