Parameter Tracking Using Energy Harvesting Sensors

Abstract

In this paper, we design an optimal sensor collaboration strategy among neighboring nodes while tracking a time-varying parameter using wireless sensor networks in the presence of imperfect communication channels. The sensor network is assumed to be self-powered, where sensors are equipped with energy harvesters that replenish energy from the environment. In order to minimize the mean square estimation error of parameter tracking, we propose an online sensor collaboration policy subject to real-time energy harvesting constraints. The proposed energy allocation strategy is computationally light and only relies on the second order statistics of the system parameters. For this, we first consider an offline non-convex optimization problem, which is solved exactly when using semidefinite programming. When the parameters of interest are temporally correlated, a penalized version of the convex-concave procedure becomes well suited for solving this problem. Based on the offline solution, we design an online power allocation policy that requires minimal online computation and satisfies the dynamics of energy flow at each sensor. We are using cooja network simulator for the simulation. We empirically show that the estimation performance of the proposed online scheme is better than that of the online scheme in the low SNR regime. Numerical results demonstrate the effectiveness of our approach.

Keywords: energy harvesting sensors, coontiki cooja, node collaboration