Sensor selection using heuristic-based sequential hypothesis testing

Sensor networks used for event detection scenarios are considered where the goal is to detect any abnormal conditions with minimum possible delay. We propose a greedy heuristic based sensor selection and sequential detection procedure that significantly improves the detection speed, measured in terms of the number of measurements needed for detection, and is suitable for distributed implementation. In the proposed model, the fusion center selects one sensor at a time for measurement while maximizing a greedy heuristic. Instead of collecting a fixed number of measurements, the fusion center collects one measurement at each time step, until by a sequential decision rule the collection stops and a decision is made. The sequential procedure significantly outperforms a non-sequential (or fixed sample size) detector in that it always needs fewer measurements on average to achieve the same detection performance.