Sparsity-promoting sensor selection with energy harvesting constraints

Abstract

In this paper, we study the sensor selection problem with energy harvesting constraints. Our goal is the minimization of the reconstruction distortion of the measured source while keeping the number of selected sensors small. We formulate this problem as an optimization problem and exploit the sparsity embedded in the problem by the use of a log-sum penalty function. Several functions are assessed, and we propose the use of strictly concave penalty functions. Finally, we provide numerical results to assess the performance of the proposed scheme.