Toward Optimal Resource Allocation: A Multi-Agent DRL Based Task Offloading Approach in Multi-UAV-Assisted MEC Networks
The application of UAV-aided MEC well-suited for the execution of the data-intensive and latency-sensitive tasks in the infrastructure-deprived regions. However, the growing number of UAVs and smart devices causing a major difficulty in the devising an effective scheme for the task offloading and re...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10551824/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850100771425615872 |
|---|---|
| author | Muhammad Naqqash Tariq Jingyu Wang Salman Raza Mohammad Siraj Majid Altamimi Saifullah Memon |
| author_facet | Muhammad Naqqash Tariq Jingyu Wang Salman Raza Mohammad Siraj Majid Altamimi Saifullah Memon |
| author_sort | Muhammad Naqqash Tariq |
| collection | DOAJ |
| description | The application of UAV-aided MEC well-suited for the execution of the data-intensive and latency-sensitive tasks in the infrastructure-deprived regions. However, the growing number of UAVs and smart devices causing a major difficulty in the devising an effective scheme for the task offloading and resource allocation in multi-UAV-aided MEC networks. Furthermore, the resource deficient environments unable to sustain prolonged resource-intensive activities, additional complexities are posed on the optimum utilization of the resources. In this paper, we introduced a multi-agent deep reinforcement learning scheme for the task offloading in the multi-UAV-assisted networks (MUAVDRL). In this configuration, the mobile users fetch computational resources from the UAVs with the goal of minimizing the computation cost which incorporates both the energy consumption and the computation delay. Initially, we start with the optimization problem which is defined as the minimizing the computational costs. Through modelling it as MDP, we aim to reduce the computational costs for mobile users. Leveraging the dynamic and high-dimensional nature of the challenge, the MUAVDRL algorithm solves this problem efficiently. Comprehensive simulation results exhibit the efficacy and superiority of our projected framework when compared to existing state-of-the-art methods, illustrating its potential in the practice. |
| format | Article |
| id | doaj-art-8e7553a7f56a4a35baf370862c457ee2 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-8e7553a7f56a4a35baf370862c457ee22025-08-20T02:40:13ZengIEEEIEEE Access2169-35362024-01-0112814288144010.1109/ACCESS.2024.341102210551824Toward Optimal Resource Allocation: A Multi-Agent DRL Based Task Offloading Approach in Multi-UAV-Assisted MEC NetworksMuhammad Naqqash Tariq0https://orcid.org/0009-0009-4868-7570Jingyu Wang1Salman Raza2https://orcid.org/0000-0003-4895-9512Mohammad Siraj3https://orcid.org/0000-0001-7805-0815Majid Altamimi4https://orcid.org/0000-0002-9431-3774Saifullah Memon5https://orcid.org/0000-0001-7736-9517State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, ChinaState Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, ChinaDepartment of Computer Science, National Textile University, Faisalabad, PakistanDepartment of Electrical Engineering, College of Engineering, King Saud University, Riyadh, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering, King Saud University, Riyadh, Saudi ArabiaDepartment of Information Technology, Quaid-e-Awam University of Engineering Science and Technology, Nawabshah, Sindh, PakistanThe application of UAV-aided MEC well-suited for the execution of the data-intensive and latency-sensitive tasks in the infrastructure-deprived regions. However, the growing number of UAVs and smart devices causing a major difficulty in the devising an effective scheme for the task offloading and resource allocation in multi-UAV-aided MEC networks. Furthermore, the resource deficient environments unable to sustain prolonged resource-intensive activities, additional complexities are posed on the optimum utilization of the resources. In this paper, we introduced a multi-agent deep reinforcement learning scheme for the task offloading in the multi-UAV-assisted networks (MUAVDRL). In this configuration, the mobile users fetch computational resources from the UAVs with the goal of minimizing the computation cost which incorporates both the energy consumption and the computation delay. Initially, we start with the optimization problem which is defined as the minimizing the computational costs. Through modelling it as MDP, we aim to reduce the computational costs for mobile users. Leveraging the dynamic and high-dimensional nature of the challenge, the MUAVDRL algorithm solves this problem efficiently. Comprehensive simulation results exhibit the efficacy and superiority of our projected framework when compared to existing state-of-the-art methods, illustrating its potential in the practice.https://ieeexplore.ieee.org/document/10551824/DRLMECresource allocationtask offloadingUAV |
| spellingShingle | Muhammad Naqqash Tariq Jingyu Wang Salman Raza Mohammad Siraj Majid Altamimi Saifullah Memon Toward Optimal Resource Allocation: A Multi-Agent DRL Based Task Offloading Approach in Multi-UAV-Assisted MEC Networks IEEE Access DRL MEC resource allocation task offloading UAV |
| title | Toward Optimal Resource Allocation: A Multi-Agent DRL Based Task Offloading Approach in Multi-UAV-Assisted MEC Networks |
| title_full | Toward Optimal Resource Allocation: A Multi-Agent DRL Based Task Offloading Approach in Multi-UAV-Assisted MEC Networks |
| title_fullStr | Toward Optimal Resource Allocation: A Multi-Agent DRL Based Task Offloading Approach in Multi-UAV-Assisted MEC Networks |
| title_full_unstemmed | Toward Optimal Resource Allocation: A Multi-Agent DRL Based Task Offloading Approach in Multi-UAV-Assisted MEC Networks |
| title_short | Toward Optimal Resource Allocation: A Multi-Agent DRL Based Task Offloading Approach in Multi-UAV-Assisted MEC Networks |
| title_sort | toward optimal resource allocation a multi agent drl based task offloading approach in multi uav assisted mec networks |
| topic | DRL MEC resource allocation task offloading UAV |
| url | https://ieeexplore.ieee.org/document/10551824/ |
| work_keys_str_mv | AT muhammadnaqqashtariq towardoptimalresourceallocationamultiagentdrlbasedtaskoffloadingapproachinmultiuavassistedmecnetworks AT jingyuwang towardoptimalresourceallocationamultiagentdrlbasedtaskoffloadingapproachinmultiuavassistedmecnetworks AT salmanraza towardoptimalresourceallocationamultiagentdrlbasedtaskoffloadingapproachinmultiuavassistedmecnetworks AT mohammadsiraj towardoptimalresourceallocationamultiagentdrlbasedtaskoffloadingapproachinmultiuavassistedmecnetworks AT majidaltamimi towardoptimalresourceallocationamultiagentdrlbasedtaskoffloadingapproachinmultiuavassistedmecnetworks AT saifullahmemon towardoptimalresourceallocationamultiagentdrlbasedtaskoffloadingapproachinmultiuavassistedmecnetworks |