Abstract: To address the problem of low detection accuracy when the fault node rate was higher than 50% in Wireless Sensor Network (WSN), a wireless sensor fault diagnosis algorithm based on the precondition of neighbor nodes and neighbor node data was proposed. Firstly, the historical data of nodes were used to pre-calculate the states of sensor nodes initially. Then the final state of each node was judged by taking advantage of similarity of nodes and pre-states of neighbor nodes. Finally, the fault node information was sent to the base station by mobile sensors through the optimal path, which effectively reduced the number of communications. A WSN was simulated in an area of 100 m*100 m. The experimental results show that compared with the traditional Distributed Fault Detection (DFD) algorithm, the diagnosis accuracy of the proposed algorithm is improved by 9.84 percentage points. Moreover, the proposed algorithm even achieves more than 95% fault diagnosis accuracy when the node failure rate is as high as 50% in the network. In practical application, the proposed algorithm improves the fault diagnosis accuracy, reduces the energy consumption effectively, and prolongs the network lifetime as well.

Key words: Wireless Sensor Network (WSN), fault diagnosis, spatiotemporal correlation, mobile sensor, optimal route selection

摘要： 针对无线传感器网络（WSN）故障节点率高于50%时故障检测率降低的问题，提出一种基于邻居节点预状态及邻居节点数据的无线传感器节点故障诊断算法。首先利用节点自身历史数据对节点状态进行初步预判断；然后结合节点间相似性和邻居节点的预状态对节点状态进行最终的判断；最后利用移动传感器节点将故障节点信息通过最优路径发送给基站，有效地减少了通信次数。仿真实验在100 m×100 m的方形区域内模拟WSN。实验结果表明，与传统的分布式故障诊断（DFD）算法相比，诊断精度提升了9.84个百分点，并且当节点故障率高达50%时，该算法仍能达到95%的诊断精度。在实际应用中，所提算法在提高故障诊断精度的同时，能有效地减少能量消耗、延长网络寿命。

关键词: 无线传感器网络, 故障诊断, 时空相关性, 移动传感器, 最优路径选择