Optimizing Resource Allocation and Task Offloading in Multi-UAV MEC Networks
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...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10967484/ |
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| author | Manzoor Ahmed Noor Fatima Salman Raza Hamid Ali Abdul Qayum Wali Ullah Khan Muhammad Sheraz Teong Chee Chuah |
| author_facet | Manzoor Ahmed Noor Fatima Salman Raza Hamid Ali Abdul Qayum Wali Ullah Khan Muhammad Sheraz Teong Chee Chuah |
| author_sort | Manzoor Ahmed |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-eac05143ce364a6fad40e2c62b7168d3 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-eac05143ce364a6fad40e2c62b7168d32025-08-20T02:18:56ZengIEEEIEEE Access2169-35362025-01-0113687106872510.1109/ACCESS.2025.356210210967484Optimizing Resource Allocation and Task Offloading in Multi-UAV MEC NetworksManzoor Ahmed0https://orcid.org/0000-0002-0459-9845Noor Fatima1Salman Raza2https://orcid.org/0000-0003-4895-9512Hamid Ali3Abdul Qayum4https://orcid.org/0000-0002-5912-2876Wali Ullah Khan5https://orcid.org/0000-0003-1485-5141Muhammad Sheraz6https://orcid.org/0000-0001-8515-2043Teong Chee Chuah7https://orcid.org/0000-0002-6285-9481School of Computer and Information Science, Institute for AI Industrial Technology Research, Hubei Engineering University, Xiaogan, ChinaDepartment of Computer Science, National Textile University, Faisalabad, PakistanDepartment of Computer Science, National Textile University, Faisalabad, PakistanDepartment of Computer Science, National Textile University, Faisalabad, PakistanDepartment of Computer Science, National Textile University, Faisalabad, PakistanInterdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Luxembourg City, LuxembourgFaculty of Artificial Intelligence and Engineering, Multimedia University, Cyberjaya, Selangor, MalaysiaFaculty of Artificial Intelligence and Engineering, Multimedia University, Cyberjaya, Selangor, MalaysiaUncrewed 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.https://ieeexplore.ieee.org/document/10967484/DRLMECresource allocationtask offloadingUAV trajectory |
| spellingShingle | Manzoor Ahmed Noor Fatima Salman Raza Hamid Ali Abdul Qayum Wali Ullah Khan Muhammad Sheraz Teong Chee Chuah Optimizing Resource Allocation and Task Offloading in Multi-UAV MEC Networks IEEE Access DRL MEC resource allocation task offloading UAV trajectory |
| title | Optimizing Resource Allocation and Task Offloading in Multi-UAV MEC Networks |
| title_full | Optimizing Resource Allocation and Task Offloading in Multi-UAV MEC Networks |
| title_fullStr | Optimizing Resource Allocation and Task Offloading in Multi-UAV MEC Networks |
| title_full_unstemmed | Optimizing Resource Allocation and Task Offloading in Multi-UAV MEC Networks |
| title_short | Optimizing Resource Allocation and Task Offloading in Multi-UAV MEC Networks |
| title_sort | optimizing resource allocation and task offloading in multi uav mec networks |
| topic | DRL MEC resource allocation task offloading UAV trajectory |
| url | https://ieeexplore.ieee.org/document/10967484/ |
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