Optimal Sensor Collaboration for Parameter Tracking Using Energy Harvesting Sensors

TL;DR

This paper develops an energy-efficient, online sensor collaboration strategy for wireless sensor networks with energy harvesting, optimizing parameter tracking accuracy under real-time energy constraints.

Contribution

It introduces a novel online power allocation policy based on offline semidefinite programming solutions, ensuring near-optimal performance with minimal online computation.

Findings

The online policy is asymptotically equivalent to the offline optimal solution.

The proposed scheme outperforms existing online schemes in low SNR conditions.

Numerical results confirm the effectiveness of the energy-aware collaboration strategy.

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 using semidefinite programming. 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 prove that the proposed online policy is asymptotically equivalent to the optimal offline solution and show its convergence rate and robustness. We empirically show that the estimation performance of the proposed online scheme is better than that of the online scheme when channel state information about the dynamical system is available in the low SNR regime. Numerical results are conducted to demonstrate the effectiveness of our approach.