Sparsity-promoting sensor selection with energy harvesting constraints

Abstract

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 nonconvex. 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 logsum penalty term, the sensor selection scheme outperforms others based on the 1 norm while making an effective use of the harvested energy.

Sparse linear regression with compressed and low-precision data via concave quadratic programming

“…Actually, the same result is obtained if each component is known to be either non-positive or non-negative: nonnegativity is assumed for simplicity without loss of generality. However, we notice that the non-negative setting has itself a number of applications, that range from sparse localization problems, see [1], to sparse sensor selection, see [2]. In [4], Problem (1) is shown to provide encouraging results if compared to state-of-the-art algorithms for the recovery of sparse signals from compressed and low-precision data.…”

Joint sensor location/power rating optimization for temporally-correlated source estimation

“…In [2]- [4], [7], [11], a static measurements model was considered such that the distributed parameter estimation is minimized based on the current measurement statistics. These works considered a source without temporal correlation.…”

“…In [2] the sensor placement via convex relaxation approach was introduced for static state estimation. In [4], [11], the same problem was solved taking into account the amount of EH This work is supported by the KAUST-MIT-TUD consortium under grant OSR-2015-Sensors-2700.…”

Joint sensor placement and power rating selection in energy harvesting wireless sensor networks

“…This design problem is solved for the parameter estimation task with estimation accuracy being the inference performance metric. A similar derivation for the error of the minimum mean squared error (MMSE) estimator is formulated in [7]. Nevertheless, only one type of sensor is considered in [7], which restricts the system flexibility.…”

“…A similar derivation for the error of the minimum mean squared error (MMSE) estimator is formulated in [7]. Nevertheless, only one type of sensor is considered in [7], which restricts the system flexibility. Moreover, the problem statement in this work is novel and accounts for a trade off between the system performance and the total budget.…”