Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/940
Title: Locating nodes in mobile sensor networks more accurately and faster
Authors: Zhang, S
Cao, J 
Chen, L
Chen, D
Keywords: Localization
Bounding-box
Wireless sensor networks
Mobility
Sequential Monte Carlo methods
Issue Date: 2008
Publisher: IEEE
Source: SECON 2008 : 5th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, Jun. 16-20, 2008, San Francisco, California, p. 37-45 How to cite?
Abstract: Localization in mobile sensor networks is more challenging than in static sensor networks because mobility increases the uncertainty of nodes' positions. Most existing localization algorithms in mobile sensor networks use Sequential Monte Carlo (SMC) methods due to their simplicity in implementation. However, SMC methods are very time-consuming because they need to keep sampling and filtering until enough samples are obtained for representing the posterior distribution of a moving node's position. In this paper, we propose a localization algorithm that can reduce the computation cost of obtaining the samples and improve the location accuracy. A simple bounding-box method is used to reduce the scope of searching the candidate samples. Inaccurate position estimations of the common neighbor nodes is used to reduce the scope of finding the valid samples and thus improve the accuracy of the obtaineed location information. Our simulation results show that, comparing with existing algorithms, our algorithm can reduce the total computation cost and increase the location accuracy. In addition, our algorithm shows several other benefits: 1) it enables each determined node to know its maximum location error, 2) it achieves higher location accuracy under higher density of common nodes, and 3) even when there are only a few anchor nodes, most nodes can still get position estimations.
URI: http://hdl.handle.net/10397/940
ISBN: 978-142-44-1777-3
Rights: © 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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