Whittle's index-based age-of-information minimization in multi-energy harvesting source networks

TL;DR

This paper introduces a Whittle's index-based scheduling policy for minimizing age-of-information in multi-energy harvesting sources, incorporating a learning approach for unknown system dynamics.

Contribution

It develops a novel WITS3 policy combining Whittle's index and threshold policies, and proposes Q-WITS3 for unknown system parameters using reinforcement learning.

Findings

WITS3 outperforms baseline policies in simulations.

Q-WITS3 effectively learns optimal policies without prior system knowledge.

The approach reduces age-of-information in energy harvesting networks.

Abstract

We consider the problem of source sampling and transmission scheduling for age-of-information minimization in a system consisting of multiple energy harvesting (EH) sources and a sink node. At each time, one of the sources is selected by the scheduler and the quality of its channel to the sink is measured. This probed channel quality is then used to decide whether a source will sample an observation and transmit the packet to the sink in that time slot. We formulate this problem as a constrained Markov decision process (CMDP) assuming i.i.d. energy arrival and channel fading processes, and relax it using a Lagrange multiplier. We apply a near optimal Whittle's index policy to decide the node to be probed. Next, for the probed node, we derive an optimal threshold policy, which recommends source sampling and observation transmission from the probed source only when the measured channel quality is above a threshold. Our proposed policy is called Whittle's index and threshold based source scheduling and sampling (WITS3) policy. However, in order to calculate Whittle's indices, one must be aware of the underlying processes' transition matrices, which are occasionally concealed from the scheduler. Therefore, we further propose a variant Q-WITS3 of WITS3 based on Q-learning assisted by two timescale asynchronous stochastic approximation, which seeks to learn Whittle's indices and optimal policies for the case with unknown channel states and EH characteristics. Numerical results demonstrate the efficacy of our algorithms over two baseline policies.