Localization algorithm for large-scale wireless sensor networks based on FCMTSR-support vector machine

Fang Zhu and Junfang Wei

International Journal of Distributed Sensor Networks, 2016, vol. 12, issue 10, 1550147716674010

Abstract: Sensor node localization is one of research hotspots in the applications of wireless sensor network field. A localization algorithm is proposed in this article which is based on improved support vector machine for large-scale wireless sensor networks. For a large-scale wireless sensor network, localization algorithm based on support vector machine faces to the problem of the large-scale learning samples. The large-scale training samples will lead to high burden of the training calculation, over learning, and low classification accuracy. In order to solve these problems, this article proposed a novel scale of training sample reduction method (FCMTSR). FCMTSR takes the training sample as point set, get the potential support vectors, and remove the non-boundary outlier data immixed by analyzing relationships between points and set. To reduce the calculation load, fuzzy C-means clustering algorithm is applied in the FCMTSR. By the FCMTSR, the training time is reduced and the localization accuracy is improved. Through the simulations, the performance of localization based on FCMTSR-support vector machine is evaluated. The results prove that the localization precision is improved 2%, the training time is reduce 55% than existing localization algorithm based on support vector machine without FCMTSR. FCMTSR-support vector machine localization algorithm also addresses the border problem and coverage hole problem effectively. Finally, the limitation of the proposed localization algorithm is discussed and future work is present.

Keywords: Wireless sensor networks; localization; support vector machine; large scale; training (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:12:y:2016:i:10:p:1550147716674010

DOI: 10.1177/1550147716674010

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