Please use this identifier to cite or link to this item: http://hdl.handle.net/11144/3701
Title: Distributed RSS-Based Localization in Wireless Sensor Networks Based on Second-Order Cone Programming
Authors: Dinis, Rui
Tomic, Slavisa
Beko, Marko
Keywords: Wireless localization
Wireless sensor network (WSN)
Received signal strength (RSS)
Second-order cone programming (SOCP) problem
Cooperative localization
Distributed localization
Issue Date: 2014
Publisher: MDPI
Abstract: In this paper, we propose a new approach based on convex optimization to address the received signal strength (RSS)-based cooperative localization problem in wireless sensor networks (WSNs). By using iterative procedures and measurements between two adjacent nodes in the network exclusively, each target node determines its own position locally. The localization problem is formulated using the maximum likelihood (ML) criterion, since ML-based solutions have the property of being asymptotically efficient. To overcome the non-convexity of the ML optimization problem, we employ the appropriate convex relaxation technique leading to second-order cone programming (SOCP). Additionally, a simple heuristic approach for improving the convergence of the proposed scheme for the case when the transmit power is known is introduced. Furthermore, we provide details about the computational complexity and energy consumption of the considered approaches. Our simulation results show that the proposed approach outperforms the existing ones in terms of the estimation accuracy for more than 1.5 m. Moreover, the new approach requires a lower number of iterations to converge, and consequently, it is likely to preserve energy in all presented scenarios, in comparison to the state-of-the-art approaches.
Peer reviewed: yes
URI: http://hdl.handle.net/11144/3701
metadata.dc.identifier.doi: 10.3390/s141018410
ISSN: 1424-8220
Publisher version: ww.mdpi.com/1424-8220/14/10/18410/pdf
Appears in Collections:DCT - Artigos/Papers

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