An Energy-Saving and Efficient Deployment Strategy for Heterogeneous Wireless Sensor Networks Based on Improved Seagull Optimization Algorithm
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Published:2023-06-02
Issue:2
Volume:8
Page:231
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ISSN:2313-7673
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Container-title:Biomimetics
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language:en
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Short-container-title:Biomimetics
Author:
Cao Li1ORCID, Wang Zihui1, Wang Zihao1, Wang Xiangkun1, Yue Yinggao12ORCID
Affiliation:
1. School of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou 325035, China 2. Intelligent Information Systems Institute, Wenzhou University, Wenzhou 325035, China
Abstract
The Internet of Things technology provides convenience for data acquisition in environmental monitoring and environmental protection and can also avoid invasive damage caused by traditional data acquisition methods. An adaptive cooperative optimization seagull algorithm for optimal coverage of heterogeneous sensor networks is proposed in order to address the issue of coverage blind zone and coverage redundancy in the initial random deployment of heterogeneous sensor network nodes in the sensing layer of the Internet of Things. Calculate the individual fitness value according to the total number of nodes, coverage radius, and area edge length, select the initial population, and aim at the maximum coverage rate to determine the position of the current optimal solution. After continuous updating, when the number of iterations is maximum, the global output is output. The optimal solution is the node’s mobile position. A scaling factor is introduced to dynamically adjust the relative displacement between the current seagull individual and the optimal individual, which improves the exploration and development ability of the algorithm. Finally, the optimal seagull individual position is fine-tuned by random opposite learning, leading the whole seagull to move to the correct position in the given search space, improving the ability to jump out of the local optimum, and further increasing the optimization accuracy. The experimental simulation results demonstrate that, compared with the coverage and network energy consumption of the PSO algorithm, the GWO algorithm, and the basic SOA algorithm, the coverage of the PSO-SOA algorithm proposed in this paper is 6.1%, 4.8%, and 1.2% higher than them, respectively, and the energy consumption of the network is reduced by 86.8%, 68.4%, and 52.6%, respectively. The optimal deployment method based on the adaptive cooperative optimization seagull algorithm can improve the network coverage and reduce the network cost, and effectively avoid the coverage blind zone and coverage redundancy in the network.
Funder
Natural Science Foundation of Zhejiang Province Wenzhou basic scientific research project Industrial Science and Technology Project of Yueqing City Wenzhou Association for Science and Technology major scientific and technological innovation projects of Wenzhou Science and Technology Plan school level scientific research projects of Wenzhou University of Technology general scientific research projects of the Provincial Department of Education teaching reform research project of Wenzhou University of Technology Wenzhou intelligent image processing and analysis key laboratory construction project
Subject
Molecular Medicine,Biomedical Engineering,Biochemistry,Biomaterials,Bioengineering,Biotechnology
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