Optimizing Resource Allocation and Task Offloading in Multi-UAV MEC Networks

Manzoor Ahmed

doi:10.1109/ACCESS.2025.3562102

Optimizing Resource Allocation and Task Offloading in Multi-UAV MEC Networks

2025-01-01

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AI

DOI：10.1109/ACCESS.2025.3562102

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Uncrewed aerial vehicle (UAV)-assisted multi-access edge computing (MEC) is increasingly being adopted to meet the rising demand for low-latency and efficient data processing, particularly in environments with limited ground infrastructure. However, optimizing task offloading and resource allocation in such networks remains a significant challenge as the number of UAVs and connected devices grows. To address this, we propose a Trajectory-Based Task Offloading in UAV-Assisted MEC (TB-TUAV) scheme, which leverages UAV mobility to enhance resource utilization and reduce latency. Unlike existing methods, TB-TUAV integrates a deep reinforcement learning (DRL) framework based on a Markov Decision Process (MDP) to dynamically optimize task offloading and resource allocation in multi-UAV networks. Our approach effectively balances exploration and exploitation, improving learning stability and ensuring fast convergence. By incorporating trajectory optimization through random path exploration, the proposed scheme efficiently distributes computational tasks while mitigating processing delays. Simulation results demonstrate that TB-TUAV significantly improves resource efficiency and reduces latency compared to state-of-the-art baseline methods. This research presents a scalable and adaptive solution for real-time MEC applications in dynamic multi-UAV environments, ensuring improved performance even under resource constraints.

Keyword:

Resource management

Autonomous aerial vehicles

Delays

Servers

Computational modeling

Vehicle dynamics

Low latency communication

Computational efficiency

Energy consumption

Dynamic scheduling

DRL

MEC

resource allocation

task offloading

UAV trajectory

Optimizing Resource Allocation and Task Offloading in Multi-UAV MEC Networks

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