Age Minimization in Massive IoT via UAV Swarm: A Multi-agent Reinforcement Learning Approach

TL;DR

This paper proposes a multi-agent deep reinforcement learning method to optimize UAV swarm deployment for IoT coverage, significantly reducing information age in large-scale networks.

Contribution

It introduces a scalable multi-agent reinforcement learning framework for UAV deployment in massive IoT scenarios, outperforming centralized methods.

Findings

Multi-agent DRL outperforms centralized DRL in large-scale IoT networks.

Cooperative and partially cooperative approaches effectively reduce information age.

The method demonstrates scalability and robustness in high-dimensional deployment problems.

Abstract

In many massive IoT communication scenarios, the IoT devices require coverage from dynamic units that can move close to the IoT devices and reduce the uplink energy consumption. A robust solution is to deploy a large number of UAVs (UAV swarm) to provide coverage and a better line of sight (LoS) for the IoT network. However, the study of these massive IoT scenarios with a massive number of serving units leads to high dimensional problems with high complexity. In this paper, we apply multi-agent deep reinforcement learning to address the high-dimensional problem that results from deploying a swarm of UAVs to collect fresh information from IoT devices. The target is to minimize the overall age of information in the IoT network. The results reveal that both cooperative and partially cooperative multi-agent deep reinforcement learning approaches are able to outperform the high-complexity centralized deep reinforcement learning approach, which stands helpless in large-scale networks.