Meta Reinforcement Learning for Strategic IoT Deployments Coverage in Disaster-Response UAV Swarms

• URL: http://arxiv.org/abs/2401.11118v1
• Date: Sat, 20 Jan 2024 05:05:39 GMT
• Title: Meta Reinforcement Learning for Strategic IoT Deployments Coverage in Disaster-Response UAV Swarms
• Authors: Marwan Dhuheir, Aiman Erbad, Ala Al-Fuqaha
• Abstract summary: Unmanned Aerial Vehicles (UAVs) have grabbed the attention of researchers in academia and industry for their potential use in critical emergency applications. These applications include providing wireless services to ground users and collecting data from areas affected by disasters. UAVs' limited resources, energy budget, and strict mission completion time have posed challenges in adopting UAVs for these applications.
• Score: 5.57865728456594
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: In the past decade, Unmanned Aerial Vehicles (UAVs) have grabbed the attention of researchers in academia and industry for their potential use in critical emergency applications, such as providing wireless services to ground users and collecting data from areas affected by disasters, due to their advantages in terms of maneuverability and movement flexibility. The UAVs' limited resources, energy budget, and strict mission completion time have posed challenges in adopting UAVs for these applications. Our system model considers a UAV swarm that navigates an area collecting data from ground IoT devices focusing on providing better service for strategic locations and allowing UAVs to join and leave the swarm (e.g., for recharging) in a dynamic way. In this work, we introduce an optimization model with the aim of minimizing the total energy consumption and provide the optimal path planning of UAVs under the constraints of minimum completion time and transmit power. The formulated optimization is NP-hard making it not applicable for real-time decision making. Therefore, we introduce a light-weight meta-reinforcement learning solution that can also cope with sudden changes in the environment through fast convergence. We conduct extensive simulations and compare our approach to three state-of-the-art learning models. Our simulation results prove that our introduced approach is better than the three state-of-the-art algorithms in providing coverage to strategic locations with fast convergence.

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