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Sensors 2017, 17(3), 487; doi:10.3390/s17030487

Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization

1
Shandong Province Key Laboratory of Wisdom Mine Information Technology, Shandong University of Science and Technology, Qingdao 266590, China
2
Shandong Provincial Key Laboratory of Computer Networks, Shandong Computer Science Center (National Supercomputer Center in Jinan), Jinan 250101, China
3
Computer Science Department, Michigan Technological University, Houghton, MI 49931, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Mohamed F. Younis
Received: 24 December 2016 / Revised: 28 February 2017 / Accepted: 28 February 2017 / Published: 1 March 2017
(This article belongs to the Section Sensor Networks)
View Full-Text   |   Download PDF [840 KB, uploaded 1 March 2017]   |  

Abstract

Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm. View Full-Text
Keywords: wireless sensor networks; particle swarm optimization; localization; parameter selection; performance comparison wireless sensor networks; particle swarm optimization; localization; parameter selection; performance comparison
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Cui, H.; Shu, M.; Song, M.; Wang, Y. Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization. Sensors 2017, 17, 487.

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