IECE - Institute of Emerging and Computer Engineers

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    IECE Transactions on Internet of Things
    Print ISSN: 0000-0000 | Online ISSN: 0000-0000
    Editor-in-Chief: Jinchao Chen
    Impact Factor: -
    CiteScore: -
    IECE Transactions on Internet of Things publishes articles on the latest advances, as well as review articles, on the various aspects of IoT. Topics include IoT system architecture, IoT enabling technologies, IoT communication and networking protocols such as network coding, and IoT services and applications. Examples are IoT demands, impacts, and implications on sensors technologies, big data management, and future Internet design for various IoT use cases, such as smart cities, smart environments, smart homes, etc. The fields of interest include: IoT architecture such as things-centric, data-centric, service-oriented IoT architecture; IoT enabling technologies and systematic integration such as sensor technologies, big sensor data management, and future Internet design for IoT; IoT services, applications, and test-beds such as IoT service middleware, IoT application programming interface (API), IoT application design, and IoT trials/experiments.
    E-mail：cjc@nwpu.edu.cn
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    Feature Paper Article
    Adaptive Binary Particle Swarm Optimization for WSN Node Optimal Deployment Algorithm
    by
    Yujiang Li
    ,
    Jinghua Cao
    IECE Transactions on Internet of Things pp.1-11 Vol. 1, No.1 DOI:10.00000/TIOT.2023.100001
    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 introdu... [...] Read more.
    Received 2022-10-09; Accepted 2022-10-17; Issue published 2022-10-17