An Improved Soft Actor–Critic Task Offloading and Edge Computing Resource Allocation Algorithm for Image Segmentation Tasks in the Internet of Vehicles

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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Bibliographic Details
Main Authors: Wei Zou, Haitao Yu, Boran Yang, Aohui Ren, Wei Liu
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:World Electric Vehicle Journal
Subjects:
edge computing
image segmentation
task offloading
computation resource allocation
deep reinforcement learning
soft actor–critic
Online Access:https://www.mdpi.com/2032-6653/16/7/353
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Summary: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.
ISSN:2032-6653

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