ADAPTIVE LINK SELECTION STRATEGIES FOR DISTRIBUTED ESTIMATION IN DIFFUSION WIRELESS NETWORKS

In this work, we propose adaptive link selection strategies for distributed estimation in diffusion-type wireless networks. We develop an exhaustive search-based link selection algorithm and a sparsity-inspired link selection algorithm that can exploit the topology of networks with poor-quality links. In the exhaustive search-based algorithm, we choose the set of neighbors that results in the smallest mean square error (MSE) for a specific node. In the sparsity-inspired link selection algorithm, a convex regularization is introduced to devise a sparsity-inspired link selection algorithm. The proposed algorithms have the ability to equip diffusion-type wireless networks and to significantly improve their performance. Simulation results illustrate that the proposed algorithms have lower MSE values, a better convergence rate and significantly improve the network performance when compared with existing methods.