A Collaborative Computation and Offloading for Compute-Intensive and Latency-Sensitive Dependency-Aware Tasks in Dew-Enabled Vehicular Fog Computing: A Federated Deep Q-Learning Approach-Reference-Cited by-同舟云学术

A Collaborative Computation and Offloading for Compute-Intensive and Latency-Sensitive Dependency-Aware Tasks in Dew-Enabled Vehicular Fog Computing: A Federated Deep Q-Learning Approach

Published:2023-12 Issue:4 Volume:20 Page:4600-4614
ISSN:1932-4537
Container-title:IEEE Transactions on Network and Service Management
language:
Short-container-title:IEEE Trans. Netw. Serv. Manage.

Author:

Mishra Kaushik¹,Rajareddy Goluguri N. V.¹^ORCID,Ghugar Umashankar¹,Chhabra Gurpreet Singh¹^ORCID,Gandomi Amir H.²^ORCID

Affiliation:

1. Department of Computer Science and Engineering, GITAM (Deemed to be University), Visakhapatnam, India

2. Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia

Funder

Óbuda University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications

Link

http://xplorestaging.ieee.org/ielx7/4275028/10355751/10143987.pdf?arnumber=10143987

Reference35 articles.

1. Vehicular cloud computing: Architectures, applications, and mobility

2. Secure and Privacy-Aware Cloud-Assisted Video Reporting Service in 5G-Enabled Vehicular Networks

3. Task offloading in vehicular fog computing: State-of-the-art and open issues

4. Resource Allocation for Vehicular Fog Computing Using Reinforcement Learning Combined With Heuristic Information

5. Mobile-Edge Computation Offloading for Ultradense IoT Networks

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