Low-latency Scheduling Approach for Dependent Tasks in MEC-enabled 5G Vehicular Networks-Reference-Cited by-同舟云学术

Low-latency Scheduling Approach for Dependent Tasks in MEC-enabled 5G Vehicular Networks

Published:2023 Issue: Volume: Page:1-1
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Wang Zhiying¹,Sun Gang¹^ORCID,Su Hanyue¹,Yu Hongfang¹^ORCID,Lei Bo²,Guizani Mohsen³^ORCID

Affiliation:

1. Key Laboratory of Optical Fiber Sensing and Communications (Ministry of Education), University of Electronic Science and Technology of China, Chengdu, China

2. China Telecom Corporation Limited Research Institute, Beijing, China

3. Machine Learning Department, Mohamed Bin Zayed University Of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE

Funder

National Key Research and Development Program 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/ielx7/6488907/6702522/10234628.pdf?arnumber=10234628

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1. Two-Tier Efficient QoE Optimization for Partitioning and Resource Allocation in UAV-Assisted MEC;Sensors;2024-07-16

2. Real-Time and Low-Overhead Graph Task Scheduling over Vehicular Computing-Assisted Edge Networks;ICC 2024 - IEEE International Conference on Communications;2024-06-09

3. Reinforcement Learning-Based Joint Beamwidth and Beam Alignment Interval Optimization in V2I Communications;Sensors;2024-01-27

4. A C-ITS Architecture for MEC and Cloud Native Back-End Services;IEEE Access;2024

5. Profit Maximization of Independent Task Offloading in MEC-Enabled 5G Internet of Vehicles;IEEE Transactions on Intelligent Transportation Systems;2024