An Improved Soft Actor–Critic Task Offloading and Edge Computing Resource Allocation Algorithm for Image Segmentation Tasks in the Internet of Vehicles
This paper addresses the challenge of offloading resource-intensive image segmentation tasks and allocating computing resources within the Internet of Vehicles (IoV) using edge-based AI. To overcome the limitations of onboard computing in smart vehicles, this study develops an efficient edge computi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/16/7/353 |
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| author | Wei Zou Haitao Yu Boran Yang Aohui Ren Wei Liu |
| author_facet | Wei Zou Haitao Yu Boran Yang Aohui Ren Wei Liu |
| author_sort | Wei Zou |
| collection | DOAJ |
| description | This paper addresses the challenge of offloading resource-intensive image segmentation tasks and allocating computing resources within the Internet of Vehicles (IoV) using edge-based AI. To overcome the limitations of onboard computing in smart vehicles, this study develops an efficient edge computing resource allocation system. The core of this system is an improved model-free soft actor–critic (iSAC) algorithm, which is enhanced by incorporating prioritized experience replay (PER). This PER-iSAC algorithm is designed to accelerate the learning process, maintain stability, and improve the efficiency and accuracy of computation offloading. Furthermore, an integrated computing and networking scheduling framework is employed to minimize overall task completion time. Simulation experiments were conducted to compare the PER-iSAC algorithm against baseline algorithms (Standard SAC and PPO). The results demonstrate that the proposed PER-iSAC significantly reduces task allocation error rates and optimizes task completion times. This research offers a practical engineering solution for enhancing the computational capabilities of IoV systems, thereby contributing to the development of more responsive and reliable autonomous driving applications. |
| format | Article |
| id | doaj-art-e586b1055f7e44cdaace657d8cd5ae18 |
| institution | DOAJ |
| issn | 2032-6653 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj-art-e586b1055f7e44cdaace657d8cd5ae182025-08-20T02:47:14ZengMDPI AGWorld Electric Vehicle Journal2032-66532025-06-0116735310.3390/wevj16070353An Improved Soft Actor–Critic Task Offloading and Edge Computing Resource Allocation Algorithm for Image Segmentation Tasks in the Internet of VehiclesWei Zou0Haitao Yu1Boran Yang2Aohui Ren3Wei Liu4School of Artificial Intelligence, Chongqing University of Technology, Chongqing 401135, ChinaChina Satellite Network Exploration Co., Ltd., Chongqing 401121, ChinaSchool of Artificial Intelligence, Chongqing University of Technology, Chongqing 401135, ChinaSchool of Artificial Intelligence, Chongqing University of Technology, Chongqing 401135, ChinaSchool of Artificial Intelligence, Chongqing University of Technology, Chongqing 401135, ChinaThis paper addresses the challenge of offloading resource-intensive image segmentation tasks and allocating computing resources within the Internet of Vehicles (IoV) using edge-based AI. To overcome the limitations of onboard computing in smart vehicles, this study develops an efficient edge computing resource allocation system. The core of this system is an improved model-free soft actor–critic (iSAC) algorithm, which is enhanced by incorporating prioritized experience replay (PER). This PER-iSAC algorithm is designed to accelerate the learning process, maintain stability, and improve the efficiency and accuracy of computation offloading. Furthermore, an integrated computing and networking scheduling framework is employed to minimize overall task completion time. Simulation experiments were conducted to compare the PER-iSAC algorithm against baseline algorithms (Standard SAC and PPO). The results demonstrate that the proposed PER-iSAC significantly reduces task allocation error rates and optimizes task completion times. This research offers a practical engineering solution for enhancing the computational capabilities of IoV systems, thereby contributing to the development of more responsive and reliable autonomous driving applications.https://www.mdpi.com/2032-6653/16/7/353edge computingimage segmentationtask offloadingcomputation resource allocationdeep reinforcement learningsoft actor–critic |
| spellingShingle | Wei Zou Haitao Yu Boran Yang Aohui Ren Wei Liu An Improved Soft Actor–Critic Task Offloading and Edge Computing Resource Allocation Algorithm for Image Segmentation Tasks in the Internet of Vehicles World Electric Vehicle Journal edge computing image segmentation task offloading computation resource allocation deep reinforcement learning soft actor–critic |
| title | An Improved Soft Actor–Critic Task Offloading and Edge Computing Resource Allocation Algorithm for Image Segmentation Tasks in the Internet of Vehicles |
| title_full | An Improved Soft Actor–Critic Task Offloading and Edge Computing Resource Allocation Algorithm for Image Segmentation Tasks in the Internet of Vehicles |
| title_fullStr | An Improved Soft Actor–Critic Task Offloading and Edge Computing Resource Allocation Algorithm for Image Segmentation Tasks in the Internet of Vehicles |
| title_full_unstemmed | An Improved Soft Actor–Critic Task Offloading and Edge Computing Resource Allocation Algorithm for Image Segmentation Tasks in the Internet of Vehicles |
| title_short | An Improved Soft Actor–Critic Task Offloading and Edge Computing Resource Allocation Algorithm for Image Segmentation Tasks in the Internet of Vehicles |
| title_sort | improved soft actor critic task offloading and edge computing resource allocation algorithm for image segmentation tasks in the internet of vehicles |
| topic | edge computing image segmentation task offloading computation resource allocation deep reinforcement learning soft actor–critic |
| url | https://www.mdpi.com/2032-6653/16/7/353 |
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