Sparsity-promoting sensor selection with energy harvesting constraints

In this paper, we propose a novel sensor selection scheme for networks equipped with energy harvesting sensing devices. Ultimately, the goal is to minimize the reconstruction distortion at the fusion center by selecting a reduced (i.e., sparse) yet informative enough subset of sensors. The solution must also fulfill the causality constraints associated to the energy harvesting process. For a classical formulation, the optimization problem turns out to be non-convex. To circumvent that, we promote sparsity directly in the power allocation vector by introducing a log-sum penalty term in the cost function. The problem can be iteratively solved by resorting to majorization-minimization procedure leading to a stationary point of the solution. Numerical results reveal that, by using a log-sum penalty term, the sensor selection scheme outperforms others based on the ℓ1 norm while making an effective use of the harvested energy.