Collaborative Optimization Strategy for Dependent Task Offloading in Vehicular Edge Computing
The advancement of the Internet of Autonomous Vehicles has facilitated the development and deployment of numerous onboard applications. However, the delay-sensitive tasks generated by these applications present enormous challenges for vehicles with limited computing resources. Moreover, these tasks...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/12/23/3820 |
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| author | Xiting Peng Yandi Zhang Xiaoyu Zhang Chaofeng Zhang Wei Yang |
| author_facet | Xiting Peng Yandi Zhang Xiaoyu Zhang Chaofeng Zhang Wei Yang |
| author_sort | Xiting Peng |
| collection | DOAJ |
| description | The advancement of the Internet of Autonomous Vehicles has facilitated the development and deployment of numerous onboard applications. However, the delay-sensitive tasks generated by these applications present enormous challenges for vehicles with limited computing resources. Moreover, these tasks are often interdependent, preventing parallel computation and severely prolonging completion times, which results in substantial energy consumption. Task-offloading technology offers an effective solution to mitigate these challenges. Traditional offloading strategies, however, fall short in the highly dynamic environment of the Internet of Vehicles. This paper proposes a task-offloading scheme based on deep reinforcement learning to optimize the strategy between vehicles and edge computing resources. The task-offloading problem is modeled as a Markov Decision Process, and an improved twin-delayed deep deterministic policy gradient algorithm, LT-TD3, is introduced to enhance the decision-making process. The integration of LSTM and a self-attention mechanism into the LT-TD3 network boosts its capability for feature extraction and representation. Additionally, considering task dependency, a topological sorting algorithm is employed to assign priorities to subtasks, thereby improving the efficiency of task offloading. Experimental results demonstrate that the proposed strategy significantly reduces task delays and energy consumption, offering an effective solution for efficient task processing and energy saving in autonomous vehicles. |
| format | Article |
| id | doaj-art-a76b261f1ce04daf925994e610730e51 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-a76b261f1ce04daf925994e610730e512024-12-13T16:27:52ZengMDPI AGMathematics2227-73902024-12-011223382010.3390/math12233820Collaborative Optimization Strategy for Dependent Task Offloading in Vehicular Edge ComputingXiting Peng0Yandi Zhang1Xiaoyu Zhang2Chaofeng Zhang3Wei Yang4School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, ChinaSchool of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, ChinaLiaoning Liaohe Laboratory, Shenyang 110033, ChinaSchool of Information and Electronic Engineering, Advanced Institute of Industrial Technology, Tokyo 140-0011, JapanSchool of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, ChinaThe advancement of the Internet of Autonomous Vehicles has facilitated the development and deployment of numerous onboard applications. However, the delay-sensitive tasks generated by these applications present enormous challenges for vehicles with limited computing resources. Moreover, these tasks are often interdependent, preventing parallel computation and severely prolonging completion times, which results in substantial energy consumption. Task-offloading technology offers an effective solution to mitigate these challenges. Traditional offloading strategies, however, fall short in the highly dynamic environment of the Internet of Vehicles. This paper proposes a task-offloading scheme based on deep reinforcement learning to optimize the strategy between vehicles and edge computing resources. The task-offloading problem is modeled as a Markov Decision Process, and an improved twin-delayed deep deterministic policy gradient algorithm, LT-TD3, is introduced to enhance the decision-making process. The integration of LSTM and a self-attention mechanism into the LT-TD3 network boosts its capability for feature extraction and representation. Additionally, considering task dependency, a topological sorting algorithm is employed to assign priorities to subtasks, thereby improving the efficiency of task offloading. Experimental results demonstrate that the proposed strategy significantly reduces task delays and energy consumption, offering an effective solution for efficient task processing and energy saving in autonomous vehicles.https://www.mdpi.com/2227-7390/12/23/3820vehicular edge computingInternet of Autonomous Vehiclesdeep reinforcement learningMarkov decision |
| spellingShingle | Xiting Peng Yandi Zhang Xiaoyu Zhang Chaofeng Zhang Wei Yang Collaborative Optimization Strategy for Dependent Task Offloading in Vehicular Edge Computing Mathematics vehicular edge computing Internet of Autonomous Vehicles deep reinforcement learning Markov decision |
| title | Collaborative Optimization Strategy for Dependent Task Offloading in Vehicular Edge Computing |
| title_full | Collaborative Optimization Strategy for Dependent Task Offloading in Vehicular Edge Computing |
| title_fullStr | Collaborative Optimization Strategy for Dependent Task Offloading in Vehicular Edge Computing |
| title_full_unstemmed | Collaborative Optimization Strategy for Dependent Task Offloading in Vehicular Edge Computing |
| title_short | Collaborative Optimization Strategy for Dependent Task Offloading in Vehicular Edge Computing |
| title_sort | collaborative optimization strategy for dependent task offloading in vehicular edge computing |
| topic | vehicular edge computing Internet of Autonomous Vehicles deep reinforcement learning Markov decision |
| url | https://www.mdpi.com/2227-7390/12/23/3820 |
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