Sensor Selection and Power Allocation Strategies for Energy Harvesting Wireless Sensor Networks

Abstract

Abstract-In this paper, we investigate the problem of jointly selecting a predefined number of energy-harvesting (EH) sensors and computing the optimal power allocation. The ultimate goal is to minimize the reconstruction distortion at the fusion center. This optimization problem is, unfortunately, non-convex. To circumvent that, we propose two suboptimal strategies: (i) a joint sensor selection and power allocation (JSS-EH) scheme that, we prove, is capable of iteratively finding a stationary solution of the original problem from a sequence of surrogate convex problems; and (ii) a separate sensor selection and power allocation (SS-EH) scheme, on which basis we can identify a sensible sensor selection and analytically find a power allocation policy by solving a convex problem. We also discuss the interplay between the two strategies. Performance in terms of reconstruction distortion, impact of initialization, actual subsets of selected sensors and computed power allocation policies, etc., is assessed by means of computer simulations. To that aim, an EH-agnostic sensor selection strategy, a lower bound on distortion, and an online version of the SS-EH and JSS-EH schemes are derived and used for benchmarking.

Random access policies for wireless networked control systems with energy harvesting sensors

“…However, these constraints are coupled across time slots and cannot be directly introduced into the stochastic optimization problem (12). In order to circumvent this, we consider the long-term behavior of the energy causality constraints (13), which, by recursively substituting the battery dynamics (14) in (13) can be written as follows…”

“…The study of communication systems powered by energy harvesting has recently received considerable attention. Current results available in the literature range from throughput maximization [3]- [6], source-channel coding [7]- [10], estimation [11]- [13], and others (see [14] for a comprehensive overview). However, in general, limited attention has been given to the use of energy harvesting technologies in control applications.…”

Sensor placement and resource allocation for energy harvesting IoT networks

“…In [1,12,8,10,16,22], a static measurement model for a vector of unknown sources was considered such that the distributed parameter estimation is minimized based on the current measurement statistics. These works considered a source without temporal correlation.…”

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.…”