Optimizing Energy and Data Collection in UAV-aided IoT Networks using Attention-based Multi-Objective Reinforcement Learning

• URL: http://arxiv.org/abs/2601.14092v1
• Date: Tue, 20 Jan 2026 15:55:11 GMT
• Title: Optimizing Energy and Data Collection in UAV-aided IoT Networks using Attention-based Multi-Objective Reinforcement Learning
• Authors: Babacar Toure, Dimitrios Tsilimantos, Omid Esrafilian, Marios Kountouris,
• Abstract summary: Unmanned Aerial Vehicles (UAVs) are becoming increasingly essential for wireless network services, particularly for data harvesting tasks.<n>Existing algorithms suffer from limited training data, which hampers their performance in highly dynamic environments.<n>We propose an attention-based Multi-Objective Reinforcement Learning architecture that explicitly handles the trade-off between data collection and energy consumption in urban environments.
• Score: 7.900374101465939
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to their adaptability and mobility, Unmanned Aerial Vehicles (UAVs) are becoming increasingly essential for wireless network services, particularly for data harvesting tasks. In this context, Artificial Intelligence (AI)-based approaches have gained significant attention for addressing UAV path planning tasks in large and complex environments, bridging the gap with real-world deployments. However, many existing algorithms suffer from limited training data, which hampers their performance in highly dynamic environments. Moreover, they often overlook the inherently multi-objective nature of the task, treating it in an overly simplistic manner. To address these limitations, we propose an attention-based Multi-Objective Reinforcement Learning (MORL) architecture that explicitly handles the trade-off between data collection and energy consumption in urban environments, even without prior knowledge of wireless channel conditions. Our method develops a single model capable of adapting to varying trade-off preferences and dynamic scenario parameters without the need for fine-tuning or retraining. Extensive simulations show that our approach achieves substantial improvements in performance, model compactness, sample efficiency, and most importantly, generalization to previously unseen scenarios, outperforming existing RL solutions.

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