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首页> 外文期刊>Journal of Sensors >Data Fusion Algorithm for Heterogeneous Wireless Sensor Networks Based on Extreme Learning Machine Optimized by Particle Swarm Optimization
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Data Fusion Algorithm for Heterogeneous Wireless Sensor Networks Based on Extreme Learning Machine Optimized by Particle Swarm Optimization

机译：基于粒子群优化优化的极端学习机的异构无线传感器网络数据融合算法

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摘要

Data fusion can reduce the data communication time between sensor nodes, reduce energy consumption, and prolong the lifetime of the network, making it an important research focus in the field of heterogeneous wireless sensor networks (HWSNs). Normal sensor nodes are susceptible to external environmental interferences, which affect the measurement results. In addition, raw data contain redundant information. The transmission of redundant information consumes excess energy, thereby reducing the lifetime of the network. We propose a data fusion method based on an extreme learning machine optimized by particle swarm optimization for HWSNs. The spatiotemporal correlation between the data of the HWSNs is determined, and the extreme learning machine method is used to process the data collected by the sensor nodes in the hierarchical routing structure of the HWSN. The particle swarm optimization algorithm is used to optimize the input weight matrix and the hidden layer bias of the extreme learning machine. An output weight matrix is created to reduce the number of hidden layer nodes and improve the generalization ability of the model. The data fusion model fuses the original data collected by the sensor nodes. The simulation results show that the proposed algorithm reduces network energy consumption and improves the lifetime of the network, the efficiency of data fusion, and the reliability of data transmission compared with other data fusion methods.
机译：数据融合可以减少传感器节点之间的数据通信时间，降低能耗，并延长网络的寿命，使其成为异构无线传感器网络（HWSNS）领域的重要研究焦点。正常传感器节点易受外部环境干扰的影响，影响测量结果。此外，原始数据包含冗余信息。冗余信息的传输消耗过量的能量，从而减少了网络的寿命。我们提出了一种基于由粒子群优化优化的极限学习机的数据融合方法。确定HWSN的数据之间的时空相关性，并且使用极端学习机方法来处理由HWSN的分层路由结构中的传感器节点收集的数据。粒子群优化算法用于优化输入权重矩阵和极端学习机的隐藏层偏置。创建输出权重矩阵以减少隐藏层节点的数量并提高模型的泛化能力。数据融合模型熔化由传感器节点收集的原始数据。仿真结果表明，该算法降低了网络能源消耗，提高了网络的寿命，数据融合效率，以及与其他数据融合方法相比的数据传输的可靠性。

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