AoI Minimization in Status Update Control with Energy Harvesting Sensors

TL;DR

This paper addresses minimizing information age in IoT systems with energy harvesting sensors using RL algorithms, modeling the problem as a Markov decision process, and demonstrating threshold-based optimal policies through simulations.

Contribution

It introduces RL algorithms for AoI minimization in energy-harvesting IoT systems and analytically characterizes optimal policies under realistic constraints.

Findings

RL algorithms significantly reduce average AoI.

Optimal policies are threshold-based.

Proposed methods outperform baselines.

Abstract

Information freshness is crucial for time-critical IoT applications, e.g., monitoring and control systems. We consider an IoT status update system with multiple users, multiple energy harvesting sensors, and a wireless edge node. The users receive time-sensitive information about physical quantities, each measured by a sensor. Users send requests to the edge node where a cache contains the most recently received measurements from each sensor. To serve a request, the edge node either commands the sensor to send a status update or retrieves the aged measurement from the cache. We aim at finding the best actions of the edge node to minimize the age of information of the served measurements. We model this problem as a Markov decision process and develop reinforcement learning (RL) algorithms: model-based value iteration and model-free Q-learning methods. We also propose a Q-learning method for the realistic case where the edge node is informed about the sensors' battery levels only via the status updates. The case under transmission limitations is also addressed. Furthermore, properties of an optimal policy are analytically characterized. Simulation results show that an optimal policy is a threshold-based policy and that the proposed RL methods significantly reduce the average cost compared to several baselines.