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Elite Opposition-Based Bare Bones Mayfly Algorithm for Optimization Wireless Sensor Networks Coverage Problem

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Abstract

Wireless sensor networks (WSNs) are composed of sensor nodes with sensing, computing and wireless communication abilities. All sensor nodes have the task of monitoring environmental conditions, collecting and transmitting data. In the field of WSNs, maximizing the coverage of the target region is a key optimization problem. Mayfly algorithm (MA) is a metaheuristic algorithm, which has been successfully implemented to solve various practical issues. This paper presents an elite opposition-based bare bones mayfly algorithm (EOBBMA), which introduces Gaussian distribution and Lévy flight, ameliorates the shortage of too many initial parameters of MA, and introduces an elite opposition-based learning (EOBL) strategy to enhance the exploration ability of the algorithm. Finally, we use EOBBMA to relocate the mobile sensors after the initial deployment, aiming at maximizing the coverage area, minimizing the redundant area and minimizing the moving distance. EOBBMA and seven other algorithms are tested in eight scales. The experimental results reveal that the coverage rate of the sensor deployment scheme obtained by EOBBMA is more than 85%, and the highest coverage is 99.6%. Considering all aspects of performance, compared with other algorithms, EOBBMA has significant superiority for WSNs coverage problem.

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The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant Nos. 62066005, U21A20464, and Program for Young Innovative Research Team in China University of Political Science and Law, under Grant No. 21CXTD02.

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Authors and Affiliations

  1. Department of Science and Technology Teaching, China University of Political Science and Law, Beijing, 102249, China

    Guo Zhou

  2. College of Artificial Intelligence, Guangxi University for Nationalities, Nanning, 530006, China

    Tian Zhang & Yongquan Zhou

  3. Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning, 530006, China

    Yongquan Zhou

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  1. Guo Zhou
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  2. Tian Zhang
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Guo Zhou: Investigation, experiment, writing-draft; Tian Zhang: Algorithm design & analysis. Yongquan Zhou: Supervision, writing-review and editing.

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Correspondence to Yongquan Zhou.

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Zhou, G., Zhang, T. & Zhou, Y. Elite Opposition-Based Bare Bones Mayfly Algorithm for Optimization Wireless Sensor Networks Coverage Problem. Arab J Sci Eng (2024). https://doi.org/10.1007/s13369-024-08899-6

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