An ADMM-Net for Data Recovery in Wireless Sensor Networks

Abstract

Data collection plays an important role in wireless sensor networks. Recovery of spatio-temporal data from incomplete sensing data is vital to the network lifetime. Many works have utilized the spatial and temporal correlations to achieve satisfactory data recovery results. However, these methods introduce large computational overhead at the fusion center. In this paper, we develop an ADMM-Net framework for correlated spatio-temporal data recovery. Both the spatial correlation and temporal correlation of sensing data are considered in a convex optimization problem, which is solved by the alternating direction method of multipliers (ADMM) algorithm. We then unfold the ADMM algorithm into a fixed-length neural network that reduces the iterations dramatically and does not require additional location information of nodes. Experimental results on a realworld dataset demonstrate that the proposed method can achieve faster convergence speed than the baseline ADMM algorithm with slight accuracy loss.