  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 excess mean square error (EMSE) 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 EMSE values, a better convergence rate and
significantly improve the network performance when compared with existing
methods.
