A signal detection scheme for wireless sensor networks based on convex optimization
Hongbo Zhao, Lei Chen, Wenquan Feng · 2016
Identifying and detecting the unknown abnormal sparse signal has become an important issue for distributed networks. In this paper, we proposed a new detection scheme based on convex optimization for wireless sensor networks. Under the Neyman-Pearson testing framework, the detection scheme first estimates the unknown signal by employing the convex optimization at the fusion center. Then the sensor nodes transmit sufficient statistics rather than raw data to the fusion center. It is verified that the proposed distributed detection scheme has performance in close proximity to ideal detector with less than one third of communication overhead compared with the centralized scheme.