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

Eslam Eldeeb; Mohammad Shehab; Hirley Alves

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arXiv · 2309.14757

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

Eslam Eldeeb·Mohammad Shehab·Hirley Alves

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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.

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BibTeX RIS

Eslam Eldeeb, Mohammad Shehab, Hirley Alves. 2023-09-26. Age Minimization in Massive IoT via UAV Swarm: A Multi-agent Reinforcement Learning Approach. https://arxiv.org/abs/2309.14757

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