Dynamic context-aware sensor selection for sequential hypothesis testing

Dynamic sensor selection rules are obtained based on a context-aware measurement model in the framework of sequential hypotheses testing. The notion of context incorporates the operational conditions that directly affect sensor measurements. While a random context leads to a Bayesian decision rule, an unknown but nonrandom context yields minimax game-based rules. In either case, the resulting sensor selection rule trades off decision performance against the cost of sensor activation and the uncertainty of the true context.