Fan Fang, Chu Shu-Chuan, Pan Jeng-Shyang, Lin Chuang, Zhao Huiqi
College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, People's Republic of China.
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, People's Republic of China.
J Appl Stat. 2021 May 26;50(3):592-609. doi: 10.1080/02664763.2021.1929089. eCollection 2023.
Aiming at the problem of fault detection in data collection in wireless sensor networks, this paper combines evolutionary computing and machine learning to propose a productive technical solution. We choose the classical particle swarm optimization (PSO) and improve it, including the introduction of a biological population model to control the population size, and the addition of a parallel mechanism for further tuning. The proposed RS-PPSO algorithm was successfully used to optimize the initial weights and biases of back propagation neural network (BPNN), shortening the training time and raising the prediction accuracy. Wireless sensor networks (WSN) has become the key supporting platform of Internet of Things (IoT). The correctness of the data collected by the sensor nodes has a great influence on the reliability, real-time performance and energy saving of the entire network. The optimized machine learning technology scheme given in this paper can effectively identify the fault data, so as to ensure the effective operation of WSN.
针对无线传感器网络数据采集中的故障检测问题，本文将进化计算与机器学习相结合，提出了一种有效的技术解决方案。我们选择经典的粒子群优化算法（PSO）并对其进行改进，包括引入生物种群模型来控制种群规模，以及添加并行机制进行进一步调优。所提出的RS-PPSO算法成功用于优化反向传播神经网络（BPNN）的初始权重和偏差，缩短了训练时间并提高了预测精度。无线传感器网络（WSN）已成为物联网（IoT）的关键支撑平台。传感器节点采集数据的正确性对整个网络的可靠性、实时性和节能性有很大影响。本文给出的优化机器学习技术方案能够有效识别故障数据，从而确保WSN的有效运行。
