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Distributed Event Localization via Alternating Direction Method of Multipliers

Research paper by Chunlei Zhang, Yongqiang Wang

Indexed on: 13 Jul '16Published on: 13 Jul '16Published in: Computer Science - Distributed; Parallel; and Cluster Computing



Abstract

This paper addresses the problem of distributed event localization using noisy range measurements with respect to sensors with known positions. Event localization is fundamental in many wireless sensor network applications such as homeland security, law enforcement, and environmental studies. However, a distributed algorithm is still lacking which can split the intensive localization computation among a wireless sensor network in which individual sensors only have limited computational capacity. Based on the alternating direction method of multipliers (ADMM), we propose a distributed event localization structure and two scalable distributed algorithms named GS-ADMM and J-ADMM respectively. More specifically, we consider a scenario in which the entire sensor network is divided into several clusters with a cluster head collecting measurements within each cluster and performing local localization. In the meantime, the cluster heads exchange intermediate computation information which will be factored into their local computations to achieve consistency (consensus) across the localization results of all cluster heads. Simulation results confirm that the proposed approaches can indeed achieve localization consistency (consensus) across the clusters and each cluster can obtain better localization performance compared with the case in which cluster heads only use local measurements within clusters.