Using sensorranks for in-network detection of faulty readings in wireless sensor networks

In this paper, the problem of determining faulty readings in a wireless sensor network without compromising detection of important events is studied. By exploring correlations between readings of sensors, a correlation network is built based on similarity between readings of two sensors. By exploring Markov Chain in the network, a mechanism for rating sensors in terms of the correlation, called SensorRank, is developed. In light of SensorRank, an efficient in-network voting algorithm, called TrustVoting, is proposed to determine faulty sensor readings. Performance studies are conducted via simulation. Experimental results show that the proposed algorithm outperforms majority voting and distance weighted voting, two state-of-the-art approaches for in-network faulty reading detection.