A Multi-Agent Reinforcement Learning Approach for Dynamic Offloading with Partial Information-Sharing in IoT Networks-Reference-Cited by-同舟云学术

A Multi-Agent Reinforcement Learning Approach for Dynamic Offloading with Partial Information-Sharing in IoT Networks

Published:2023-10-10 Issue: Volume: Page:
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Container-title:2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall)
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Author:

Zhang Jing¹,Shen Fei¹,Tang Liang¹,Yan Feng²,Qin Fei³,Wang Chao⁴

Affiliation:

1. Chinese Academy of Sciences,Key Laboratory of Wireless Sensor Network and Communications, Shanghai Institute of Microsystem and Information Technology,Shanghai,China,200050

2. Southeast University,National Mobile Communications Research Laboratory,Nanjing,China,210096

3. University of Chinese Academy of Sciences,Beijing,China,10148

4. Shanghai Huace Navigation Technology Ltd,Shanghai,201706

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10333258/10333344/10333696.pdf?arnumber=10333696

Reference11 articles.

1. JIRA: Joint Incentive Design and Resource Allocation for Edge-based Real-time Video Streaming Systems

2. EEDTO: An Energy-Efficient Dynamic Task Offloading Algorithm for Blockchain-Enabled IoT-Edge-Cloud Orchestrated Computing

3. A Memetic Algorithm for Cooperative Complex Task Offloading in Heterogeneous Vehicular Networks

4. FEMOS: Fog-Enabled Multitier Operations Scheduling in Dynamic Wireless Networks

5. Chimera: An Energy-Efficient and Deadline-Aware Hybrid Edge Computing Framework for Vehicular Crowdsensing Applications

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