Utility Optimal Scheduling in Energy Harvesting Networks

TL;DR

This paper introduces online algorithms for energy harvesting networks that optimize utility performance while managing energy storage constraints without prior knowledge of energy harvesting processes.

Contribution

The paper proposes ESA and MESA algorithms that achieve near-optimal utility with bounded congestion and storage requirements, advancing energy management in harvesting networks.

Findings

ESA achieves utility within O(ε) of optimal with bounded congestion and storage.

MESA achieves similar utility with significantly reduced storage and congestion bounds.

Algorithms operate without prior knowledge of energy harvesting processes.

Abstract

In this paper, we show how to achieve close-to-optimal utility performance in energy harvesting networks with only finite capacity energy storage devices. In these networks, nodes are capable of harvesting energy from the environment. The amount of energy that can be harvested is time varying and evolves according to some probability law. We develop an \emph{online} algorithm, called the Energy-limited Scheduling Algorithm (ESA), which jointly manages the energy and makes power allocation decisions for packet transmissions. ESA only has to keep track of the amount of energy left at the network nodes and \emph{does not require any knowledge} of the harvestable energy process. We show that ESA achieves a utility that is within $O(\epsilon)$ of the optimal, for any $\epsilon>0$, while ensuring that the network congestion and the required capacity of the energy storage devices are \emph{deterministically} upper bounded by bounds of size $O(1/\epsilon)$. We then also develop the Modified-ESA algorithm (MESA) to achieve the same $O(\epsilon)$ close-to-utility performance, with the average network congestion and the required capacity of the energy storage devices being only $O([\log(1/\epsilon)]^2)$.