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An Improved Distributed Fault Diagnosis Algorithm in Wireless Sensor Networks

机译:一种无线传感器网络的改进分布式故障诊断算法

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In wireless sensor networks (WSNs), nodes fault diagnosis is the important measure for the continuous monitoring service. Efficient and accurate methods for diagnose nodes fault is a hot research topic in the field of WSNs in the recent years. This paper propose an improved distributed fault diagnosis method namely Cross-Space Distributed Fault Diagnosis (CS-DFD) which follows the DFD algorithm, building high-dimensional vector space model through the collected information from each node, establishing the cross type of sliding window through its historical data and the data from neighbor nodes, setting the cross direction customizable weights of fault. Ultimately, achieving the goal of fault diagnosis by detecting abnormal vector and threshold. The experimental results show that it has reduced the amount of calculation; simplified harsh conditions of fault judgments; reduced the consumption of electricity, etc. which in DFD algorithm. Comparing with the traditional DFD algorithm, the fault diagnosis accuracy improved by 5.14%, the fault of false alarm ratio (FAR) decreased by 2.01% and the time of diagnosis has shortened obviously.
机译:在无线传感器网络(WSN)中,节点故障诊断是连续监控服务的重要措施。诊断节点故障的高效和准确方法是近年来WSNS领域的热门研究主题。本文提出了一种改进的分布式故障诊断方法即跨空间分布式故障诊断(CS-DFD),其遵循DFD算法,通过来自每个节点的收集的信息构建高维矢量空间模型,建立通过的交叉类型的滑动窗口其历史数据和来自邻居节点的数据,设置横向可自定义的故障权重。最终,通过检测异常向量和阈值来实现故障诊断的目标。实验结果表明它降低了计算量;简化严厉的故障判断条件;降低了在DFD算法中的电力等消耗。比较与传统的DFD算法,故障诊断精度提高5.14%,误报例(远)的故障减少2.01%,诊断时间明显缩短。

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