JO-TADP: Learning-Based Cooperative Dynamic Resource Allocation for MEC–UAV-Enabled Wireless Network
Providing robust communication services to mobile users (MUs) is a challenging task due to the dynamicity of MUs. Unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) are used to improve connectivity by allocating resources to MUs more efficiently in a dynamic environment. However, energy...
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ftdoajarticles:oai:doaj.org/article:aa8b75ed2d2c4ec3882fc06e71c75901 2023-06-11T04:10:37+02:00 JO-TADP: Learning-Based Cooperative Dynamic Resource Allocation for MEC–UAV-Enabled Wireless Network Shabeer Ahmad Jinling Zhang Adil Khan Umar Ajaib Khan Babar Hayat 2023-05-01T00:00:00Z https://doi.org/10.3390/drones7050303 https://doaj.org/article/aa8b75ed2d2c4ec3882fc06e71c75901 EN eng MDPI AG https://www.mdpi.com/2504-446X/7/5/303 https://doaj.org/toc/2504-446X doi:10.3390/drones7050303 2504-446X https://doaj.org/article/aa8b75ed2d2c4ec3882fc06e71c75901 Drones, Vol 7, Iss 303, p 303 (2023) unmanned aerial vehicle mobile edge computing anarchic federated learning artificial intelligence cooperative dynamic resource allocation Motor vehicles. Aeronautics. Astronautics TL1-4050 article 2023 ftdoajarticles https://doi.org/10.3390/drones7050303 2023-05-28T00:34:15Z Providing robust communication services to mobile users (MUs) is a challenging task due to the dynamicity of MUs. Unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) are used to improve connectivity by allocating resources to MUs more efficiently in a dynamic environment. However, energy consumption and lifetime issues in UAVs severely limit the resources and communication services. In this paper, we propose a dynamic cooperative resource allocation scheme for MEC–UAV-enabled wireless networks called joint optimization of trajectory, altitude, delay, and power (JO-TADP) using anarchic federated learning (AFL) and other learning algorithms to enhance data rate, use rate, and resource allocation efficiency. Initially, the MEC–UAVs are optimally positioned based on the MU density using the beluga whale optimization (BLWO) algorithm. Optimal clustering is performed in terms of splitting and merging using the triple-mode density peak clustering (TM-DPC) algorithm based on user mobility. Moreover, the trajectory, altitude, and hovering time of MEC–UAVs are predicted and optimized using the self-simulated inner attention long short-term memory (SSIA-LSTM) algorithm. Finally, the MUs and MEC–UAVs play auction games based on the classified requests, using an AFL-based cross-scale attention feature pyramid network (CSAFPN) and enhanced deep Q-learning (EDQN) algorithms for dynamic resource allocation. To validate the proposed approach, our system model has been simulated in Network Simulator 3.26 (NS-3.26). The results demonstrate that the proposed work outperforms the existing works in terms of connectivity, energy efficiency, resource allocation, and data rate. Article in Journal/Newspaper Beluga Beluga whale Beluga* Directory of Open Access Journals: DOAJ Articles Pyramid ENVELOPE(157.300,157.300,-81.333,-81.333) Drones 7 5 303 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
unmanned aerial vehicle mobile edge computing anarchic federated learning artificial intelligence cooperative dynamic resource allocation Motor vehicles. Aeronautics. Astronautics TL1-4050 |
spellingShingle |
unmanned aerial vehicle mobile edge computing anarchic federated learning artificial intelligence cooperative dynamic resource allocation Motor vehicles. Aeronautics. Astronautics TL1-4050 Shabeer Ahmad Jinling Zhang Adil Khan Umar Ajaib Khan Babar Hayat JO-TADP: Learning-Based Cooperative Dynamic Resource Allocation for MEC–UAV-Enabled Wireless Network |
topic_facet |
unmanned aerial vehicle mobile edge computing anarchic federated learning artificial intelligence cooperative dynamic resource allocation Motor vehicles. Aeronautics. Astronautics TL1-4050 |
description |
Providing robust communication services to mobile users (MUs) is a challenging task due to the dynamicity of MUs. Unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) are used to improve connectivity by allocating resources to MUs more efficiently in a dynamic environment. However, energy consumption and lifetime issues in UAVs severely limit the resources and communication services. In this paper, we propose a dynamic cooperative resource allocation scheme for MEC–UAV-enabled wireless networks called joint optimization of trajectory, altitude, delay, and power (JO-TADP) using anarchic federated learning (AFL) and other learning algorithms to enhance data rate, use rate, and resource allocation efficiency. Initially, the MEC–UAVs are optimally positioned based on the MU density using the beluga whale optimization (BLWO) algorithm. Optimal clustering is performed in terms of splitting and merging using the triple-mode density peak clustering (TM-DPC) algorithm based on user mobility. Moreover, the trajectory, altitude, and hovering time of MEC–UAVs are predicted and optimized using the self-simulated inner attention long short-term memory (SSIA-LSTM) algorithm. Finally, the MUs and MEC–UAVs play auction games based on the classified requests, using an AFL-based cross-scale attention feature pyramid network (CSAFPN) and enhanced deep Q-learning (EDQN) algorithms for dynamic resource allocation. To validate the proposed approach, our system model has been simulated in Network Simulator 3.26 (NS-3.26). The results demonstrate that the proposed work outperforms the existing works in terms of connectivity, energy efficiency, resource allocation, and data rate. |
format |
Article in Journal/Newspaper |
author |
Shabeer Ahmad Jinling Zhang Adil Khan Umar Ajaib Khan Babar Hayat |
author_facet |
Shabeer Ahmad Jinling Zhang Adil Khan Umar Ajaib Khan Babar Hayat |
author_sort |
Shabeer Ahmad |
title |
JO-TADP: Learning-Based Cooperative Dynamic Resource Allocation for MEC–UAV-Enabled Wireless Network |
title_short |
JO-TADP: Learning-Based Cooperative Dynamic Resource Allocation for MEC–UAV-Enabled Wireless Network |
title_full |
JO-TADP: Learning-Based Cooperative Dynamic Resource Allocation for MEC–UAV-Enabled Wireless Network |
title_fullStr |
JO-TADP: Learning-Based Cooperative Dynamic Resource Allocation for MEC–UAV-Enabled Wireless Network |
title_full_unstemmed |
JO-TADP: Learning-Based Cooperative Dynamic Resource Allocation for MEC–UAV-Enabled Wireless Network |
title_sort |
jo-tadp: learning-based cooperative dynamic resource allocation for mec–uav-enabled wireless network |
publisher |
MDPI AG |
publishDate |
2023 |
url |
https://doi.org/10.3390/drones7050303 https://doaj.org/article/aa8b75ed2d2c4ec3882fc06e71c75901 |
long_lat |
ENVELOPE(157.300,157.300,-81.333,-81.333) |
geographic |
Pyramid |
geographic_facet |
Pyramid |
genre |
Beluga Beluga whale Beluga* |
genre_facet |
Beluga Beluga whale Beluga* |
op_source |
Drones, Vol 7, Iss 303, p 303 (2023) |
op_relation |
https://www.mdpi.com/2504-446X/7/5/303 https://doaj.org/toc/2504-446X doi:10.3390/drones7050303 2504-446X https://doaj.org/article/aa8b75ed2d2c4ec3882fc06e71c75901 |
op_doi |
https://doi.org/10.3390/drones7050303 |
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Drones |
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303 |
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