Matrix Completion with Convex Constraints for Data Gathering in Wireless Sensor Networks

In this paper, we propose a novel formulation for the efficient data gathering problem in Wireless Sensor Networks (WSNs) based on Matrix Completion technique. The objective here is to optimize the usage of WSN resources during the data gathering process by taking into account an a priori knowledge about the data to be gathered. More precisely, we model the prior knowledge about the target data via hard convex constraints where the involved regularization parameters are related to some physical properties of the data itself and are then easy to interpret. Hence, the problem of data gathering is reduced to a constrained minimization one. A new class of primal- dual algorithm is extended in order to solve the resulted optimization problem. Experiments carried out on two datasets show that the proposed algorithm outperforms state-of-the-art methods and achieves a good recovery accuracy even if the sampling rate is very low.