Adaptive sensor selection for multitarget detection in Heterogeneous Sensor Networks

Resource management for multitarget detection in Heterogeneous Sensor Networks (HSN) is an open research area. By considering communication capabilities, energy differences and mobility dissimilarities jointly, we propose a fuzzy logic system (FLS) and apply fuzzy c-mean (FCM) clustering to adaptively select sensors that report surrounding targets information for further data fusion. Monte Carlo simulations reveal the optimized cluster numbers, averagely selected nodes and the performance of multitarget detection. The proposed resource management approach not only extends the overall system lifetime, but also offers an appropriate tradeoff between resource consumption and detection performance.