Performance analysis of group based detection for sparse sensor networks

In this paper, we analyze the performance of group based detection in sparse sensor networks, when the system level detection decision is made based on the detection reports generated from multiple sensing periods. Sparse deployment is essential for reducing cost of large scale sensor networks, which cover thousands of square miles. In a sparse deployment, the sensor field is only partially covered by sensors' sensing ranges, resulting in void sensing areas in the region, but all nodes are connected through multi-hop networking. Further, due to the unavoidable false alarms generated by a single sensor in a network, many deployed systems use group based detection to reduce system level false alarms. Despite the popularity of group based detection, few analysis works in the literature deal with group based detection. In this paper, we propose a novel approach called Markov chain based Spatial approach (M-S-approach) to model group based detection in sensor networks. The M-S-approach successfully overcomes the complicated conditional detection probability of a target in each sensing period, and reduces the execution time of the analysis from many days to 1 minute. The analytical model is validated through extensive simulations. This analytical work is important because it provides an easy way to understand the performance of a system that uses group based detection without running countless simulations or deploying real systems.