低空车载雾计算任务卸载的隐私保护分层强化学习框架

IF 6.3 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Open Journal of the Communications Society Pub Date : 2024-09-10 DOI:10.1109/ojcoms.2024.3457023

Zhiwei Wei, Jingxin Mao, Bing Li, Rongqing Zhang

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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Privacy-Preserving Hierarchical Reinforcement Learning Framework for Task Offloading in Low-Altitude Vehicular Fog Computing

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来源期刊

IEEE Open Journal of the Communications Society Multiple-

CiteScore

13.70

自引率

3.80%

发文量

审稿时长

10 weeks

期刊介绍： The IEEE Open Journal of the Communications Society (OJ-COMS) is an open access, all-electronic journal that publishes original high-quality manuscripts on advances in the state of the art of telecommunications systems and networks. The papers in IEEE OJ-COMS are included in Scopus. Submissions reporting new theoretical findings (including novel methods, concepts, and studies) and practical contributions (including experiments and development of prototypes) are welcome. Additionally, survey and tutorial articles are considered. The IEEE OJCOMS received its debut impact factor of 7.9 according to the Journal Citation Reports (JCR) 2023. The IEEE Open Journal of the Communications Society covers science, technology, applications and standards for information organization, collection and transfer using electronic, optical and wireless channels and networks. Some specific areas covered include: Systems and network architecture, control and management Protocols, software, and middleware Quality of service, reliability, and security Modulation, detection, coding, and signaling Switching and routing Mobile and portable communications Terminals and other end-user devices Networks for content distribution and distributed computing Communications-based distributed resources control.