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    2. IECE Transactions on Internet of Things
    3. Vol. 1, No.1
    4. DOI: 10.00000/TIOT.2023.100001
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    Academic Editor
    Jinchao Chen
    School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China
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    A Biomimetic Covering Learning Method Based on Principle of Homology Continuity Adaptive Binary Particle Swarm Optimization for WSN Node Optimal Deployment Algorithm
    Feature Paper Article
    Adaptive Binary Particle Swarm Optimization for WSN Node Optimal Deployment Algorithm
    by
    Yujiang Li 1 *
    Jinghua Cao 1
    1 School of Mathematics and Statistics, Lingnan Normal University, Zhanjiang 524048, China
    DOI：10.00000/TIOT.2023.100001
    Received 2022-10-09; Accepted 2022-10-17; Issue published 2022-10-17
    Abstract
    In order to optimize the deployment of wireless sensor network nodes, and avoid network energy consumption increase due to node redundancy and uneven coverage, the multi-objective mathematical optimization problem of area coverage is transformed into a function problem. Aiming at network coverage rate, node dormancy rate and network coverage uniformity, the idea of genetic algorithm mutation is introduced based on the discrete binary particle swarm optimization and the global optimal speed is mutated to avoid the algorithm falling into the local optimal solution. In order to further improve the optimization ability of the algorithm, the adaptive learning factor and inertia weight are introduced to obtain the optimal deployment algorithm of wireless sensor network nodes. The experimental results show that the algorithm can reduce the number of active nodes efficiently, improve coverage uniformity, reduce network energy consumption and prolong network lifetime under the premise that the coverage rate is greater than 90%, and compared with an algorithm called coverage configuration protocol, an algorithm called finding the minimum working sets in wireless sensor networks, and an algorithm called binary particle swarm optimization-g in literature, the number of active nodes in this algorithm is reduced by about 36%, 30% and 23% respectively.
    Keywords
    Uniform coverage
    discrete binary particle swarm optimization algorithm
    wireless sensor network
    optimal deployment
    Cite This Article
    L. Yujiang, C. Jinghua, and C. Jinchao, “Adaptive Binary Particle Swarm Optimization for WSN Node Optimal Deployment Algorithm”, Vol. 1, No.1, vol. 1, no. 1, pp. 1–11, 2022, doi: 10.00000/TIOT.2023.100001.
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