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Mathematical and Computational Applications is published by MDPI from Volume 21 Issue 1 (2016). Articles in this Issue were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence. Articles are hosted by MDPI on as a courtesy and upon agreement with the previous journal publisher.
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Math. Comput. Appl. 2011, 16(4), 809-818;

Energy Efficient Target Tracking with Particle Filtering Techmique in Wireless Sensor Networks

Department of Computer Engineering Celal Bayar University, 45140 Manisa, Turkey
Published: 1 December 2011
PDF [469 KB, uploaded 1 April 2016]


In this study, a new target tracking algorithm is proposed for wireless sensor networks. The aim of the algorithm is to decrease energy consumption of the system by decreasing the ratio of target misses. Next location of the target is predicted by using Particle Filtering (PF) technique which aims to represent the posterior density function by a set of random samples with associated weights. Nodes are deployed according to the hexagon shaped network topology in which each of the hexagons represents a cluster with a predetermined leader node. In order to decrease the ratio of target misses, nodes that are closer to the target's predicted location are woken up to make them ready to detect the target. This increases the probability of detecting the target by one of the neighboring hexagons when the target makes sudden turns or unexpected movements. Tracking performance of the proposed algorithm is evaluated by comparing with KNearest Cluster Tracking (KNCT), Wakening Based Target Tracking Algorithm (WBTA)[10] and Generic Static Tracking Approach (GSTA) in terms of miss ratio and energy consumption metrics.
Keywords: Wireless Sensor Networks; Target Tracking; Particle Filtering Wireless Sensor Networks; Target Tracking; Particle Filtering
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Alaybeyoglu, A. Energy Efficient Target Tracking with Particle Filtering Techmique in Wireless Sensor Networks. Math. Comput. Appl. 2011, 16, 809-818.

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