Self-diagnosis for large scale wireless sensor networks

Self-diagnosis for large scale wireless sensor networks,10.1109/INFCOM.2011.5934944,Kebin Liu,Qiang Ma,Xibin Zhao,Yunhao Liu

Self-diagnosis for large scale wireless sensor networks   (Citations: 1)
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Existing approaches to diagnosing sensor networks are generally sink-based, which rely on actively pulling state informa- tion from all sensor nodes so as to conduct centralized analysis. However, the sink-based diagnosis tools incur huge communica- tion overhead to the traffic sensitive sensor networks. Also, due to the unreliable wireless communications, sink often obtains incom- plete and sometimes suspicious information, leading to highly inaccurate judgments. Even worse, we observe that it is always more difficult to obtain state information from the problematic or critical regions. To address the above issues, we present the con- cept of self-diagnosis, which encourages each single sensor to join the fault decision process. We design a series of novel fault detec- tors through which multiple nodes can cooperate with each other in a diagnosis task. The fault detectors encode the diagnosis proc- ess to state transitions. Each sensor can participate in the fault diagnosis by transiting the detector's current state to a new one based on local evidences and then pass the fault detector to other nodes. Having sufficient evidences, the fault detector achieves the Accept state and outputs the final diagnosis report. We examine the performance of our self-diagnosis tool called TinyD2 on a 100 nodes testbed.
Conference: IEEE INFOCOM - INFOCOM , pp. 1539-1547, 2011
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