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The accurate and low-cost localization of sensors using a wireless sensornetwork is critically required in a wide range of today's applications. Wepropose a novel, robust maximum likelihood-type method for distributedcooperative received signal strength-based localization in wireless sensornetworks. To cope with mixed LoS/NLoS conditions, we model the measurementsusing a two-component Gaussian mixture model. The relevant channel parameters,including the reference path loss, the path loss exponent and the variance ofthe measurement error, for both LoS and NLoS conditions, are assumed to beunknown deterministic parameters and are adaptively estimated. Unlike existingalgorithms, the proposed method naturally takes into account the (possible)asymmetry of links between nodes. The proposed approach has a communicationoverhead upper-bounded by a quadratic function of the number of nodes andcomputational complexity scaling linearly with it. The convergence of theproposed method is guaranteed for compatible network graphs and compatibilitycan be tested a priori by restating the problem as a graph coloring problem.Simulation results, carried out in comparison to a centralized benchmarkalgorithm, demonstrate the good overall performance and high robustness inmixed LoS/NLoS environments.

Robust Distributed Cooperative RSS-based Localization for Directed Graphs in Mixed LoS/NLoS Environments