Sensor Fusion for Distributed Detection of Mobile Intruders in Surveillance Wireless Sensor Networks

Wireless sensor networks are widely applicable in surveillance applications for intruder detection. Many research articles in the literature have focused on to generate an inference about the intruder detection and evaluated this in terms of detection probability and false alarm rate. In this paper, we have solved the problem of passive mobile intruder detection using two modalities i.e. acoustic signal model and sensing probability model. A three-level hierarchy is proposed to generate an inference about the presence of a mobile intruder. At base level the deployed sensor nodes are grouped together using k-mean clustering. For both modalities the distance between the sensor nodes and intruder is computed. At cluster head, the received signal strengths or sensing probabilities reported by the sensor nodes are employed for binary hypothesis testing. The cluster heads transmit their decisions to the fusion center for the generation of accurate inference about the intruder detection after performing Likelihood Ratio Test (LRT) on the fused decisions. Numerical analysis of received signals identify the optimum threshold value for detection probability computation. The derived fusion rule maximizes detection probability with respect to the allowable false alarm rates. The accuracy of proposed fusion rule is depending on the number of actual detections reported by the deployed sensor nodes. Simulation results also show that the derived fusion rule performs better as compared to other fusion rules in terms of sensor count, speed of mobile intruder, detection probability and detection accuracy.