| Online Distributed Fault Detection of Sensor Measurements |
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| 作者单位： | Key Laboratory of Computer System and Architecture Institute of Computing Technology，Chinese Academy of Sciences，Key Laboratory of Computer System and Architecture，Institute of Computing Technology，Chinese Academy of Sciences，Key Laboratory of Computer System and Architecture，Institute of Computing Technology，Chinese Academy of Sciences，Beijing 100080 Graduate School of Chinese Academy of Sciences，Beijing 100039，China，Beijing 100080，Beijing 100080 China |
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| 摘 要： | In wireless sensor networks (WSNs), a faulty sensor may produce incorrect data and transmit them to the other sensors. This would consume the limited energy and bandwidth of WSNs. Furthermore, the base station may make inappropriate decisions when it receives the incorrect data sent by the faulty sensors. To solve these problems, this paper develops an online distributed algorithm to detect such faults by exploring the weighted majority vote scheme. Considering the spatial correlations in WSNs, a faulty sensor can diagnose itself through utilizing the spatial and time information provided by its neighbor sensors. Simulation results show that even when as many as 30% of the sensors are faulty, over 95% of faults can be correctly detected with our algorithm. These results indicate that the proposed algorithm has excellent performance in detecting fault of sensor measurements in WSNs.
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Online Distributed Fault Detection of Sensor Measurements |
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| Authors: | GAO Jianliang XU Yongjun LI Xiaowei |
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| Abstract: | In wireless sensor networks (WSNs), a faulty sensor may produce incorrect data and transmit them to the other sensors. This would consume the limited energy and bandwidth of WSNs. Furthermore, the base station may make inappropriate decisions when it receives the incorrect data sent by the faulty sensors. To solve these problems, this paper develops an online distributed algorithm to detect such faults by exploring the weighted majority vote scheme. Considering the spatial correlations in WSNs, a faulty sensor can diagnose itself through utilizing the spatial and time information provided by its neighbor sensors. Simulation results show that even when as many as 30% of the sensors are faulty, over 95% of faults can be correctly detected with our algorithm. These results indicate that the proposed algorithm has excellent performance in detecting fault of sensor measurements in WSNs. |
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| Keywords: | wireless sensor networks fault diagnosis fault detection fault tolerance spatial correlation |
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