Energy-Efficient Censoring Strategy for Distributed Multi-View Sparse Signal Recovery in WSNs

We consider a classic compressed sensing–based wireless sensor network (WSN) where a large number of sensors observe a common sparse signal. Under the partial-view assumption, each sensor acquires only a portion of the signal, with its support restricted to a unknown subset. To maintain energy efficiency, every sensor employs a sparse sensing vector to measure the (possibly masked) signal and applies the ternary censoring rule proposed in [1] to decide whether its measurement is informative; only qualified measurements are transmitted to the fusion center. The multi-view nature of this setting poses challenges to reconstruction stability as different sensors observe distinct partial views of the signal. To address this, we propose an algorithm, called CSC-modSP, which dynamically refines the qualified measurements to enhance the overall signal reconstruction quality. Simulation results validate the effectiveness of the proposed method.