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    Author 2
    ORCID
    0000-0003-0244-7191
    Yujiang Li
    School of Mathematics and Statistics, Lingnan Normal University, Zhanjiang 524048, China
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    Free Access | Research Article | 22 September 2024
    An Event-Triggered Energy-Efficient Wireless Routing Protocol for Fault Monitoring of Wind Turbines
    by
    Yujiang Li
    ,
    Peng Xu
    ,
    Wenqing Chen
    ,
    Hongli Zhang
    IECE Transactions on Internet of Things | Volume 2, Issue 3: 55-62, 2024 | DOI:10.62762/TIOT.2024.257019
    Abstract
    Monitoring the health condition of wind turbines is crucial to ensure the safety and efficient operation of wind farms. Wireless sensor networks (WSNs) provide an economical and effective solution for such monitoring. However, when sensors detect faults in wind turbines, traditional WSN routing protocols often lead to redundant data transmission, resulting in energy waste. To address this issue, an event-triggered energy-efficient wireless routing protocol (EEWRP) is proposed specifically in this paper for wind turbine fault monitoring. The protocol improves the distributed energy-efficient clustering algorithm (DEEC) by first identifying the type of event and then using an adaptive dynamic... More >
    Graphical Abstract
    An Event-Triggered Energy-Efficient Wireless Routing Protocol for Fault Monitoring of Wind Turbines
    Research Article | Feature Paper | 17 April 2023 | Cited: 11
    Adaptive Binary Particle Swarm Optimization for WSN Node Optimal Deployment Algorithm
    by
    Yujiang Li
    ,
    Jinghua Cao
    IECE Transactions on Internet of Things | Volume 1, Issue 1: 1-8, 2023 | DOI:10.62762/TIOT.2023.564457
    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... More >
    Graphical Abstract
    Adaptive Binary Particle Swarm Optimization for WSN Node Optimal Deployment Algorithm