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Large-scale mobile wireless sensor network data fusion algorithm

机译:大规模移动无线传感器网络数据融合算法

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摘要

In the process of data collection of large-scale delay tolerant mobile wireless sensor networks (MWSN), in order to reduce the energy consumption of sensor network data transmission, time consuming and low efficiency problems, a data fusion algorithm for mobile wireless sensor networks based on improved RBF neural network is proposed. The data fusion model is introduced into the RBF neural network model to determine the adaptive vector value, which can improve the efficiency and accuracy of the data fusion model. Simulation results show that the data fusion algorithm proposed in this paper is more effective than LEACH protocol, BP neural network data fusion algorithm, which effectively reduce the transmission of real-time data, improve the efficiency and reliability of the network, and provide a better energy efficiency for the network, more suitable for large-scale deployment, collection of intensive data MWSN.
机译:在大规模时延容忍的移动无线传感器网络(MWSN)的数据收集过程中,为了减少传感器网络数据传输的能耗,耗时和低效率的问题,一种基于移动无线传感器网络的数据融合算法提出了一种改进的RBF神经网络。将数据融合模型引入RBF神经网络模型中,确定自适应矢量值,可以提高数据融合模型的效率和准确性。仿真结果表明,本文提出的数据融合算法比LEACH协议,BP神经网络数据融合算法更有效,有效减少了实时数据的传输,提高了网络的效率和可靠性,并提供了更好的解决方案。对于网络能源效率而言,更适合大规模部署,收集密集数据的MWSN。

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