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Efficient Estimator for Distributed RSS-based Localization in Wireless Sensor Networks

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Bibliographic Details
Title: Efficient Estimator for Distributed RSS-based Localization in Wireless Sensor Networks
Authors: Lipovac, Vladimir
Publisher Information: 2015.
Publication Year: 2015
Subject Terms: distributed localization, Wireless localization, wireless sensor network (WSN), received signal strength (RSS), cooperative localization, second-order cone programming problem (SOCP)
Description: We address the received signal strength (RSS) based target localization problem in large-scale cooperative wireless sensor networks (WSNs). Using the noisy RSS measurements, we formulate the localization problem based on the maximum likelihood (ML) criterion. Although MLbased solutions have asymptotically optimal performance, the derived localization problem is non-convex. To overcome this difficulty, we propose a convex relaxation leading to second-order cone programming (SOCP) estimator, which can be solved efficiently by interior-point algorithms. Moreover, we investigate the case where target nodes limit the number of cooperating nodes by selecting only those neighbors with the highest RSS. This simple procedure can reduce the energy consumption of an algorithm in both communication and computation phase. Our simulation results show that the proposed approach outperforms significantly the existing ones in terms of the estimation accuracy and convergence. Furthermore, the new approach does not suffer significant performance degradation when the number of cooperating nodes is reduced.
Document Type: Conference object
Accession Number: edsair.dris...01492..29707570d823337f5e899e554f7b628c
Database: OpenAIRE
Description
Abstract:We address the received signal strength (RSS) based target localization problem in large-scale cooperative wireless sensor networks (WSNs). Using the noisy RSS measurements, we formulate the localization problem based on the maximum likelihood (ML) criterion. Although MLbased solutions have asymptotically optimal performance, the derived localization problem is non-convex. To overcome this difficulty, we propose a convex relaxation leading to second-order cone programming (SOCP) estimator, which can be solved efficiently by interior-point algorithms. Moreover, we investigate the case where target nodes limit the number of cooperating nodes by selecting only those neighbors with the highest RSS. This simple procedure can reduce the energy consumption of an algorithm in both communication and computation phase. Our simulation results show that the proposed approach outperforms significantly the existing ones in terms of the estimation accuracy and convergence. Furthermore, the new approach does not suffer significant performance degradation when the number of cooperating nodes is reduced.