Name
Abstract | ||
|---|---|---|
Existing approaches to diagnosing sensor networks are generally sink based, which rely on actively pulling state information from sensor nodes so as to conduct centralized analysis. First, sink-based tools incur huge communication overhead to the traffic-sensitive sensor networks. Second, due to the unreliable wireless communications, sink often obtains incomplete and suspicious information, leading to inaccurate judgments. Even worse, it is always more difficult to obtain state information from problematic or critical regions. To address the given issues, we present a novel self-diagnosis approach, which encourages each single sensor to join the fault decision process. We design a series of fault detectors through which multiple nodes can cooperate with each other in a diagnosis task. Fault detectors encode the diagnosis process to state transitions. Each sensor can participate in the diagnosis by transiting the detector's current state to a new state based on local evidences and then passing the detector to other nodes. Having sufficient evidences, the fault detector achieves the Accept state and outputs a final diagnosis report. We examine the performance of our self-diagnosis tool called TinyD2 on a 100-node indoor testbed and conduct field studies in the GreenOrbs system, which is an operational sensor network with 330 nodes outdoor. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TWC.2014.2336653 | IEEE Transactions on Wireless Communications |
Keywords | DocType | Volume |
Self-diagnosis,wireless sensor networks (WSNs),traffic-sensitive sensor network,fault detector,sink-based tool,telecommunication network reliability,fault decision process,network diagnosis,large-scale wireless sensor network,self-diagnosis tool,diagnosing sensor network,failure detection system,GreenOrbs system,fault diagnosis,TinyD2,state transitions,wireless sensor networks,telecommunication traffic,accept state,indoor radio,100-node indoor testbed | Journal | 13 |
Issue | ISSN | Citations |
10 | 1536-1276 | 1 |
PageRank | References | Authors |
0.35 | 20 | 5 |