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Multi-Sensor Data Fusion Algorithm Based on Trust Degree and Improved Genetics

机译：基于信任度和改进遗传算法的多传感器数据融合算法

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摘要

Aiming at the problems of low data fusion precision and poor stability in greenhouse wireless sensor networks (WSNs), a multi-sensor data fusion algorithm based on trust degree and improved genetics is proposed. The original data collected by the sensor nodes are sent to the gateway through the sink node, and data preprocessing based on cubic exponential smoothing is performed at the gateway to eliminate abnormal data and noise data. In fuzzy theory, the range of membership functions is determined, according to this feature, the data fusion algorithm based on exponential trust degree is used to fuse the smooth data to avoid the absolute degree of mutual trust between data. In this paper, we have improved the crossover and mutation operations in the standard genetic algorithm, the variation is separated from the intersection, the chaotic sequence is used to determine the intersection, and the weakest single-point intersection is implemented to improve the convergence accuracy of the algorithm, weaken and avoid jitter problems during optimization. The chaotic sequence is used to mutate multiple genes in the chromosome to avoid premature algorithm maturity. Finally, the improved genetic algorithm is used to optimize the fusion estimation value. The experimental results show that the cubic exponential smoothing can significantly reduce the data fluctuation and improve the stability of the system. Compared with the commonly used data fusion algorithms such as arithmetic average method and adaptive weighting method, the data fusion algorithm based on trust degree and improved genetics has higher fusion precision. At the same time, the execution time of the algorithm is greatly reduced.
机译：针对温室无线传感器网络（WSNs）数据融合精度低，稳定性差的问题，提出了一种基于信任度和改进遗传学的多传感器数据融合算法。传感器节点收集的原始数据通过接收器节点发送到网关，并在网关执行基于三次指数平滑的数据预处理，以消除异常数据和噪声数据。在模糊理论中，确定隶属函数的范围，根据该特征，采用基于指数信任度的数据融合算法融合平滑数据，避免了数据之间的绝对互信度。在本文中，我们改进了标准遗传算法中的交叉和变异操作，将变异与交叉点分离，使用混沌序列确定交叉点，并实现最弱单点交叉点以提高收敛精度在优化过程中减弱和避免抖动问题。混沌序列用于突变染色体中的多个基因，以避免算法过早成熟。最后，使用改进的遗传算法对融合估计值进行优化。实验结果表明，三次指数平滑可以显着减少数据波动，提高系统的稳定性。与算术平均法和自适应加权法等常用数据融合算法相比，基于信任度和改进遗传学的数据融合算法具有更高的融合精度。同时大大减少了算法的执行时间。

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