An ADMM-ResNet for data recovery in wireless sensor networks with guaranteed convergence

Data collection is a basic application of wireless sensor networks (WSNs). In practice, only a subset of sensor nodes is selected for data sensing and transmission due to the bandwidth constraint of the channel, energy constraint of the nodes, or malfunctions of the nodes. Data recovery from incomplete sensing data is vital to WSNs. Many works perform data recovery by utilizing the low-rank property of the spatio-temporal correlated data. However, these methods usually converge slowly to achieve satisfactory accuracy performance. In this paper, we propose an ADMM-ResNet framework based on residual networks (ResNets) for spatio-temporal correlated data recovery. The formulated optimization problem is solved by the alternating direction method of multipliers (ADMM) algorithm. The updates of auxiliary variable in the ADMM algorithm can be replaced by ResNets, and the ADMM algorithm is unrolled into a fixed-length neural network. The proposed ADMM-ResNet significantly reduces the number of iterations compared with traditional ADMM algorithm. We theoretically prove that the proposed ADMM-ResNet can globally converge to a fixed-point. Experimental results verify the theoretical convergence and demonstrate the effectiveness. (C) 2020 Elsevier Inc. All rights reserved.

Automated summary

Data collection is a basic application of wireless sensor networks (WSNs), and data recovery from incomplete sensing data is vital to WSNs. This paper proposes an ADMM-ResNet framework based on residual networks for spatio-temporal correlated data recovery, significantly reducing the number of iterations compared with traditional ADMM algorithm, and theoretically proving global convergence to a fixed-point.