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When large sensor networks are applied to the task of target tracking, it is necessary to successively identify subsets of sensors that are most useful at each time instant. Such a task involves simultaneously maximizing target detection accuracy and minimizing querying cost, addressed in this paper by the application of multi-objective evolutionary algorithms (MOEAs). NSGA-II, a well-known MOEA, is demonstrated to be successful in obtaining diverse solutions (trade-off points), when compared to a ”weighted sum” approach that combines both objectives into a single cost function. We also explore an improvement, LS-DNSGA, which incorporates periodic local search into the algorithm, and outperforms standard NSGA-II on the sensor selection problem.

DYNAMIC AND EVOLUTIONARY MULTI-OBJECTIVE OPTIMIZATION FOR SENSOR SELECTION IN SENSOR NETWORKS FOR TARGET TRACKING