A Deep-Reinforcement-Learning-Based Computation Offloading With Mobile Vehicles in Vehicular Edge Computing
Author:
Affiliation:
1. School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, China
2. School of Software Engineering, Xi’an Jiaotong University, Xi’an, China
3. College of Computer Science and Technology, Qingdao University, Qingdao, China
Funder
K. C. Wong Education Foundation
Fundamental Research Funds for the Central Universities
Natural Science Basic Research Plan in Shaanxi Province of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing
Link
http://xplorestaging.ieee.org/ielx7/6488907/10227658/10091540.pdf?arnumber=10091540
Reference59 articles.
1. A novel Latency-Guaranteed based Resource Double Auction for market-oriented edge computing
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3. Multiobjective Optimized Cloudlet Deployment and Task Offloading for Mobile-Edge Computing
4. A Deep Reinforcement Learning-Based Dynamic Computational Offloading Method for Cloud Robotics
5. RLbR: A reinforcement learning based V2V routing framework for offloading 5G cellular IoT
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1. Joint Optimization of Multiuser Computation Offloading and Wireless-Caching Resource Allocation With Linearly Related Requests in Vehicular Edge Computing System;IEEE Internet of Things Journal;2024-01-01
2. Airborne Computing: A Toolkit for UAV-Assisted Federated Computing for Sustainable Smart Cities;IEEE Internet of Things Journal;2023-11-01
3. Dynamic task offloading and collaborative task execution using three tier edge cloud computing (T2EC2) system for autonomous vehicles;Journal of Intelligent & Fuzzy Systems;2023-09-13
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