DRL-driven zero-RIS assisted energy-efficient task offloading in vehicular edge computing networks
The increasing complexity of modern automotive applications presents difficulties when running them on the on-board units (OBUs) of vehicles. While 5G/6G vehicular edge computing networks (VECNs) offer potential solutions through computation task offloading, ensuring prompt, energy-efficient access...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157823003919 |
| _version_ | 1797389537415004160 |
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| author | Muhammad Ayzed Mirza Junsheng Yu Manzoor Ahmed Salman Raza Wali Ullah Khan Fang Xu Ali Nauman |
| author_facet | Muhammad Ayzed Mirza Junsheng Yu Manzoor Ahmed Salman Raza Wali Ullah Khan Fang Xu Ali Nauman |
| author_sort | Muhammad Ayzed Mirza |
| collection | DOAJ |
| description | The increasing complexity of modern automotive applications presents difficulties when running them on the on-board units (OBUs) of vehicles. While 5G/6G vehicular edge computing networks (VECNs) offer potential solutions through computation task offloading, ensuring prompt, energy-efficient access to these networks remains a significant challenge. To overcome these challenges, reconfigurable intelligent surfaces (RIS) can play an important role in 6G vehicular networks. With RIS, networks can provide better connectivity, increased data rate and energy efficient access, and communication channel security. In this paper, we utilize zero-energy RIS (ze-RIS) to aid vehicular computation offloading while maximizing the energy and time savings while meeting the task and environmental constraints. A joint power and offloading mechanism controlling DRL-driven RIS-assisted energy efficient task offloading (DREEO) scheme is proposed. DREEO utilizes a hybrid approach that combines binary and partial offloading mechanisms, complemented by an intelligent communication link switching mechanism. This strategy helps in saving both energy and time effectively. An efficiency factor, serving as both a performance indicator and a reward function, is introduced for the DRL agent, considering both saved energy and time. Through extensive evaluations, DREEO scheme shown an increase in task success rate from 2.13% to 7.36% and has improved the efficiency factor from 21.97 to 51.27. Furthermore, compared to other evaluated schemes, the DREEO scheme consistently outperforms them in terms of reward and the TFPS ratio, the DRL properties. |
| first_indexed | 2024-03-08T22:57:25Z |
| format | Article |
| id | doaj.art-547d4886639041cba0a18f3a9c579ee6 |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-03-08T22:57:25Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-547d4886639041cba0a18f3a9c579ee62023-12-16T06:06:07ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782023-12-013510101837DRL-driven zero-RIS assisted energy-efficient task offloading in vehicular edge computing networksMuhammad Ayzed Mirza0Junsheng Yu1Manzoor Ahmed2Salman Raza3Wali Ullah Khan4Fang Xu5Ali Nauman6BUPT-QMUL EM Theory and Application International Research Lab, Beijing University of Posts and Telecommunications, Beijing 100876, China; School of Computer and Information Science and also with Institute for AI Industrial Technology Research, Hubei Engineering University, Xiaogan City 432000, China; Department of Computer Science, Superior University, Lahore, PakistanBUPT-QMUL EM Theory and Application International Research Lab, Beijing University of Posts and Telecommunications, Beijing 100876, China; School of Physics and Electronic Information, Anhui Normal University, Wuhu 241003, China; School of Intelligence and Digital Engineering, Luoyang Vocational College of Science and Technology, Luoyang 471000, ChinaSchool of Computer and Information Science and also with Institute for AI Industrial Technology Research, Hubei Engineering University, Xiaogan City 432000, China; Corresponding author.Department of Computer Science, National Textile University, Faisalabad, PakistanInterdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Luxembourg City, 1855, LuxembourgSchool of Computer and Information Science and also with Institute for AI Industrial Technology Research, Hubei Engineering University, Xiaogan City 432000, ChinaDepartment of Information and Communication Engineering, Yeungnam University, Republic of KoreaThe increasing complexity of modern automotive applications presents difficulties when running them on the on-board units (OBUs) of vehicles. While 5G/6G vehicular edge computing networks (VECNs) offer potential solutions through computation task offloading, ensuring prompt, energy-efficient access to these networks remains a significant challenge. To overcome these challenges, reconfigurable intelligent surfaces (RIS) can play an important role in 6G vehicular networks. With RIS, networks can provide better connectivity, increased data rate and energy efficient access, and communication channel security. In this paper, we utilize zero-energy RIS (ze-RIS) to aid vehicular computation offloading while maximizing the energy and time savings while meeting the task and environmental constraints. A joint power and offloading mechanism controlling DRL-driven RIS-assisted energy efficient task offloading (DREEO) scheme is proposed. DREEO utilizes a hybrid approach that combines binary and partial offloading mechanisms, complemented by an intelligent communication link switching mechanism. This strategy helps in saving both energy and time effectively. An efficiency factor, serving as both a performance indicator and a reward function, is introduced for the DRL agent, considering both saved energy and time. Through extensive evaluations, DREEO scheme shown an increase in task success rate from 2.13% to 7.36% and has improved the efficiency factor from 21.97 to 51.27. Furthermore, compared to other evaluated schemes, the DREEO scheme consistently outperforms them in terms of reward and the TFPS ratio, the DRL properties.http://www.sciencedirect.com/science/article/pii/S1319157823003919Vehicular edge computing networks (VECNs)Reconfigurable intelligent surface (RIS)Task offloadingDeep reinforcement learning (DRL)5G/6G |
| spellingShingle | Muhammad Ayzed Mirza Junsheng Yu Manzoor Ahmed Salman Raza Wali Ullah Khan Fang Xu Ali Nauman DRL-driven zero-RIS assisted energy-efficient task offloading in vehicular edge computing networks Journal of King Saud University: Computer and Information Sciences Vehicular edge computing networks (VECNs) Reconfigurable intelligent surface (RIS) Task offloading Deep reinforcement learning (DRL) 5G/6G |
| title | DRL-driven zero-RIS assisted energy-efficient task offloading in vehicular edge computing networks |
| title_full | DRL-driven zero-RIS assisted energy-efficient task offloading in vehicular edge computing networks |
| title_fullStr | DRL-driven zero-RIS assisted energy-efficient task offloading in vehicular edge computing networks |
| title_full_unstemmed | DRL-driven zero-RIS assisted energy-efficient task offloading in vehicular edge computing networks |
| title_short | DRL-driven zero-RIS assisted energy-efficient task offloading in vehicular edge computing networks |
| title_sort | drl driven zero ris assisted energy efficient task offloading in vehicular edge computing networks |
| topic | Vehicular edge computing networks (VECNs) Reconfigurable intelligent surface (RIS) Task offloading Deep reinforcement learning (DRL) 5G/6G |
| url | http://www.sciencedirect.com/science/article/pii/S1319157823003919 |
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