Statistical Edge Detection with Distributed Sensors under the Neynan-Pearson (NP) Optimality

A statistical approach to distributed edge region detection in wireless sensor networks, which is optimized under the Neyman-Pearson (NP) criterion, is proposed in this work. The concept of edge nodes is adopted to label the defined edge region. Even though statistical methods have been proposed to detect edge nodes, a rigorous way to select the threshold value is lacking. Based on the NP criterion, a decision-fusion approach is developed to address the problem of threshold selection. Performance comparison of the proposed approach and the classifier-based approach is conducted. Simulation results show that the proposed approach is more stable and outperforms the classifier-based approach when there is a location error