SSMA:用于优化无线传感器网络覆盖问题的简化黏菌算法

IF 3.2 Q2 AUTOMATION & CONTROL SYSTEMS Systems Science & Control Engineering Pub Date : 2022-07-13 DOI:10.1080/21642583.2022.2084650
Yuanye Wei, Xiuxi Wei, Huajuan Huang, Jian Bi, Yongquan Zhou, Yanlian Du
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引用次数: 6

摘要

无线传感器网络(WSN)覆盖问题是考虑如何最大限度地扩大网络覆盖范围,以获得可靠的监测和跟踪服务,并保证服务质量。本文提出了一种求解WSN覆盖问题的简化黏菌算法(SSMA)。在SSMA中,我们主要进行了13组无线传感器网络覆盖优化实验,并将其与六种著名的元启发式优化算法进行了比较。实验结果和Wilcoxon秩和检验表明,所提出的SSMA总体上具有竞争力,性能和有效性突出。我们提出的SSMA算法有助于有效控制网络节点能量,提高感知服务质量,延长网络生存时间。
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SSMA: simplified slime mould algorithm for optimization wireless sensor network coverage problem
Wireless sensor network (WSN) coverage problem is to think about how to maximize the network coverage to obtain reliable monitoring and tracking services with guaranteed quality of service. In this paper, a simplified slime mould algorithm (SSMA) for solving the WSN coverage problem is proposed. In SSMA, we mainly conducted 13 groups of WSNs coverage optimization experiments and compared them with six well-known meta-heuristic optimization algorithms. The experimental results and Wilcoxon rank-sum test show that the proposed SSMA is generally competitive, outstanding performance and effectiveness. We proposed SSMA algorithm could be helpful to effectively control the network nodes energy, improve the perceived quality of services and extend the network survival time.
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来源期刊
Systems Science & Control Engineering
Systems Science & Control Engineering AUTOMATION & CONTROL SYSTEMS-
CiteScore
9.50
自引率
2.40%
发文量
70
审稿时长
29 weeks
期刊介绍: Systems Science & Control Engineering is a world-leading fully open access journal covering all areas of theoretical and applied systems science and control engineering. The journal encourages the submission of original articles, reviews and short communications in areas including, but not limited to: · artificial intelligence · complex systems · complex networks · control theory · control applications · cybernetics · dynamical systems theory · operations research · systems biology · systems dynamics · systems ecology · systems engineering · systems psychology · systems theory
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