Eigenvector Centrality-Based Mobile Target Tracking in Wireless Sensor Networks

Abstract

We propose an eigenvector centrality-based tracking algorithm to trace the trajectory of a mobile Radioactive Dispersal Device (RDD) in a wireless sensor network. The sink constructs an adjacency matrix in which the entry for edge (i, j) is the sum of the signal strengths reported by sensor nodes i and j in their respective neighborhoods over a sampling time period. The sink uses this adjacency matrix as the basis to determine the Eigenvector Centralities (EVC) of the vertices with respect to the radioactive signals sensed in the neighborhood. We hypothesize that sensor nodes that have a high EVC (suspect nodes) for the sampling time period are within the vicinity of the RDD within that period. We propose that the arithmetic mean (calculated by the sink) of the X and Y coordinates of the suspect sensor nodes be considered as the predicted location of the RDD at a time instant corresponding to the middle of the sampling time period. We evaluate the difference between the predicted and exact locations of the RDD trajectory over time as a function of different operating parameters.