Abstract
Exchanging too many messages for fault detection will cause not only a degradation of the network quality of service, but also represents a huge burden on the limited energy of sensors. Therefore, we propose an uncertainty-based distributed fault detection through aided judgment of neighbors for wireless sensor networks. The algorithm considers the serious influence of sensing measurement loss and therefore uses Markov decision processes for filling in missing data. Most important of all, fault misjudgments caused by uncertainty conditions are the main drawbacks of traditional distributed fault detection mechanisms. We draw on the experience of evidence fusion rules based on information entropy theory and the degree of disagreement function to increase the accuracy of fault detection. Simulation results demonstrate our algorithm can effectively reduce communication energy overhead due to message exchanges and provide a higher detection accuracy ratio.
Highlights
Sensors can be rapidly deployed into large areas and perform monitoring tasks by autonomous wireless communication methods
Stage 4: A node whose tendency status is Un determines the actual state by using entropy-based evidence combination mechanism: 20: Node i (i {LF,Un}) receives the evidence of good neighbors. 21: Combine the evidences generated by measurements by adopting information entropy-based evidence fusion, and acquire the combined belief probability assignment (BPA) functions m* ({G}), m* ({F}), and m* ( ) ; 22: Node i finds the node j which matches the min m j ( G m* ({G})) ; 23: if cij = 1, Zi = FAULT, else Zi = GOOD; 24: end if 25: Determined node broadcasts its status if it’s a good sensor
We propose a fault detection mechanism for wireless sensor networks based on data filling and evidence fusion methods
Summary
Sensors can be rapidly deployed into large areas and perform monitoring tasks by autonomous wireless communication methods. Neighbor cooperation is one approach to fault detection, whereby a sensor uses neighbor measurements to decide its own fault state collaboratively [4,5,6]. This is demonstrated to be efficacious for fault information collection and diagnosis because it alleviates the overheads of sink nodes or base stations in order to avoid network bottlenecks. The rest of the paper is organized as follows: Section 2 describes some related works in the area of fault detection in WSNs. Section 3 introduces our Uncertainty-based Distributed Fault Detection algorithm (uDFD) and the concrete mechanisms involved.
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