Hidden Gaussian Markov Model for Distribued Fault Detection in Wireless Sensor Networks

International audienceWireless Sensor Networks (WSN) are based on a large number of sensor nodesused to measure informations like temperature, acceleration, displacement orpressure. The measurements are used to estimate the state of the monitoredsystem or area. However, the quality of the measurements must be guaranteedto ensure the reliability of the estimated state of the system. Actually, sensorscan be used in a hostile environment such as, on a battle field in the presence offires, floods, earthquakes, for example. In these environments as well as in normaloperation,sensors can fail.The failure of sensor nodes can also be caused by other factors like: the failure ofmodule (such as sensing module) due to the fabrication process models, batterypower losts and so on. A WSN must be able to identify faulty nodes. Thereforewe propose a probabilistic approach based on Hidden Markov Model to identifyfaulty sensor nodes. Our proposed approach predicts the future state of each nodefrom its actual state, so the fault could be detect before it occurs. We use an aidedjudgment of neighbor sensor nodes in the network. The algorithm analyses thecorrelation of the sensors’ data with respect to its neighborhood. A systematicapproach to divide a network on cliques is proposed to fully draw the neighborhoodof each node in the network. After drawing the neighborhood of each node (cliques),damaged cliques are identified using Gaussian distribution theorem. Finally, we usethe Hidden Markov model to identify faulty nodes in the identified damaged cliquesby calculating the probability of each node to stay in its normal state. Simulationresults demonstrate our algorithm is efficient even for a huge wireless sensornetwork unlike previous approaches