Distributed RSS-based localization in wireless sensor networks using convex relaxation

In this paper we address the sensor localization problem in large-scale wireless sensor networks (WSNs) by using the received signal strength (RSS) measurements. Finding the maximum likelihood (ML) estimate involves solving a non-convex optimization problem, thus making the search for the globally optimal solution hard. Based on the second-order cone programming (SOCP) relaxation, two methods which solve the localization problem in a completely distributed manner are proposed. Computer simulations show that the proposed approaches work well in various scenarios, and efficiently solve the localization problem.