A primal-dual algorithm for data gathering based on matrix completion for Wireless Sensor Networks

Mohamed Ali Moussa, Yosra Marnissi, Yacine Ghamri-Doudane

Abstract: In this paper, we proposed a novel matrix completion algorithm in Wireless Sensor Networks. Our contribution is twofold. First, we propose a general formulation of the problem of data gathering as a tractable convex optimization problem on the Hilbert space of data measurement matrices. The proposed criterion takes full advantage of both the global features, particularly low rank nature of data, and local features namely the sparsity and the spatio-temporal correlation in the sensors data to improve the recovery accuracy. Second, we design a new class of primal-dual optimization approach in order to optimize the resulting regularized criterion. Experiments carried out on two datasets show that the proposed algorithm outperforms state-of-the-art methods for low sampling rate and achieves a good recovery accuracy even if the sampling rate is very low.