Abstract

In this paper we investigate distributed localization of an intruder in wireless sensor networks (WSNs), in which the
sensor nodes (SNs) censor their transmission to the fusion center (FC). The SNs locally detect the intruder and send their decision only if it is positive. The FC, on the other hand, uses those binary data to localize the intruder. We present the censored maximum likelihood (cML) localization algorithm, Furthermore, we derive two computationally simple localization algorithms, the quadratic approximate ML (QAML) and the linear approximate ML (LAML). The performance of the ML-based algorithms significantly outperforms the heuristics-based algorithms, such as the centroid method (CM) and the center of maximum enclosing rectangle (CMER), as the simulation results show.