Energy Sharing for Multiple Sensor Nodes with Finite Buffers

TL;DR

This paper develops and compares advanced algorithms for optimal energy sharing among sensor nodes with finite buffers, aiming to minimize data transmission delays in energy harvesting sensor networks.

Contribution

It introduces novel reinforcement learning and optimization algorithms for energy sharing, addressing the challenge of large state-action spaces in sensor networks.

Findings

Algorithms outperform heuristic greedy methods

State-action space aggregation improves computational efficiency

Near-optimal policies significantly reduce data transmission delays

Abstract

We consider the problem of finding optimal energy sharing policies that maximize the network performance of a system comprising of multiple sensor nodes and a single energy harvesting (EH) source. Sensor nodes periodically sense the random field and generate data, which is stored in the corresponding data queues. The EH source harnesses energy from ambient energy sources and the generated energy is stored in an energy buffer. Sensor nodes receive energy for data transmission from the EH source. The EH source has to efficiently share the stored energy among the nodes in order to minimize the long-run average delay in data transmission. We formulate the problem of energy sharing between the nodes in the framework of average cost infinite-horizon Markov decision processes (MDPs). We develop efficient energy sharing algorithms, namely Q-learning algorithm with exploration mechanisms based on the $\epsilon$-greedy method as well as upper confidence bound (UCB). We extend these algorithms by incorporating state and action space aggregation to tackle state-action space explosion in the MDP. We also develop a cross entropy based method that incorporates policy parameterization in order to find near optimal energy sharing policies. Through simulations, we show that our algorithms yield energy sharing policies that outperform the heuristic greedy method.