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. The fuzzy logic processing methodology, to the best of our knowledge, is used for the first time for multitarget detection in HSN. Monte Carlo simulations illustrate the answers to the following questions: 1) To divide nodes into how many clusters is the optimum for this system? 2) How many and who are the nodes in the selected cluster? 3) What's the multitarget detection performance? The proposed resource management approach not only extends the overall system lifetime, but also offers an appropriate tradeoff between resource consumption and detection performance.
sensor selection multitarget heterogeneous sensor networks fuzzy logic