Optimal Allocation of Time-Resources for Multihypothesis Activity-Level Detection

The optimal allocation of samples for activity-level detection in a wireless body area network for health-monitoring applications is considered. A wireless body area network with heterogeneous sensors is deployed in a simple star topology with the fusion center receiving biometric samples from each of the sensors. The number of samples collected from each of the sensors is optimized to minimize the probability of misclassification between multiple hypotheses at the fusion center. Using experimental data from our pilot study, we find equally allocating samples amongst sensors is normally suboptimal. A lower probability of error can be achieved by allocating a greater fraction of the samples to sensors which can better discriminate between certain activity-levels. As the number of samples is an integer, prior work employed an exhaustive search to determine the optimal allocation of integer samples. However, such a search is computationally expensive. To this end, an alternate continuous-valued vector optimization is derived which yields approximately optimal allocations which can be found with significantly lower complexity.