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

Huanqing Cui,Min Song,Minglei Shu,Yinglong Wang

doi:10.3390/s17030487

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.

Highlights

Wireless sensor networks (WSNs) are an important infrastructure of the Internet of Things used for sensing the surrounding information, whose applications can be classified into monitoring and tracking in the fields of military and public [1]
This paper focuses on the Particle swarm optimization (PSO)-based localization algorithms for static WSNs, where all sensors are static after deployment
In order to analyze the impacts of network setups on localization performance, Constricted PSO (CPSO) is taken as an example, because we find that the other algorithms have the same trends as CPSO, except the variation range

Summary

Introduction

Wireless sensor networks (WSNs) are an important infrastructure of the Internet of Things used for sensing the surrounding information, whose applications can be classified into monitoring and tracking in the fields of military and public [1]. In these applications, spatial information is one of the most important contexts of the sensed data, and the location information can support the coverage, routing, and many other operations of a WSN. Only a part of sensor nodes (named anchors) are equipped with GPS devices These anchors serve as references to the other nodes (named unknown nodes), which are to be localized.

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Conclusion