资源受限IIoT计算环境下基于深度强化学习的AI任务卸载

IF 8.9 1区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Internet of Things Journal Pub Date : 2025-10-10 DOI:10.1109/jiot.2025.3620126

Deshui Yu, Xianhui Liu, Jia Ning, Shengyi Wang, Chenglin Zhu, Weidong Zhao

引用次数: 0

摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Deep Reinforcement Learning-Based AI Task Offloading in Resource-Constrained IIoT Computing Environments

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来源期刊

IEEE Internet of Things Journal Computer Science-Information Systems

CiteScore

17.60

自引率

13.20%

发文量

1982

期刊介绍： The EEE Internet of Things (IoT) Journal publishes articles and review articles covering various aspects of IoT, including IoT system architecture, IoT enabling technologies, IoT communication and networking protocols such as network coding, and IoT services and applications. Topics encompass IoT's impacts on sensor technologies, big data management, and future internet design for applications like smart cities and smart homes. Fields of interest include IoT architecture such as things-centric, data-centric, service-oriented IoT architecture; IoT enabling technologies and systematic integration such as sensor technologies, big sensor data management, and future Internet design for IoT; IoT services, applications, and test-beds such as IoT service middleware, IoT application programming interface (API), IoT application design, and IoT trials/experiments; IoT standardization activities and technology development in different standard development organizations (SDO) such as IEEE, IETF, ITU, 3GPP, ETSI, etc.