4.7 Article

Multi-Agent DRL-Based Hungarian Algorithm (MADRLHA) for Task Offloading in Multi-Access Edge Computing Internet of Vehicles (IoVs)

Related references

Note: Only part of the references are listed.
Article Automation & Control Systems

Adaptive Digital Twin and Multiagent Deep Reinforcement Learning for Vehicular Edge Computing and Networks

Ke Zhang et al.

Summary: Technological advancements in urban informatics and vehicular intelligence have made smart vehicles ubiquitous edge computing platforms for various applications. However, the different capacities of smart vehicles, diverse application requirements, and unpredictable vehicular topology pose challenges for efficient edge computing services. To address these challenges, we propose incorporating digital twin technology and artificial intelligence into a vehicular edge computing network, enabling centralized service matching and distributed task offloading and resource allocation using multiagent deep reinforcement learning. We also introduce a coordination graph-driven task offloading scheme that integrates service matching and intelligent offloading scheduling in both digital twin and physical networks to minimize costs. Numerical results based on real urban traffic datasets demonstrate the efficiency of our proposed schemes.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2022)

Article Computer Science, Information Systems

Multiagent Deep-Reinforcement-Learning-Based Resource Allocation for Heterogeneous QoS Guarantees for Vehicular Networks

Jie Tian et al.

Summary: In this article, a multi-agent deep reinforcement learning-based resource allocation framework is proposed to satisfy the heterogeneous QoS requirements in vehicular networks. The framework combines centralized learning and decentralized execution to optimize channel allocation and power control.

IEEE INTERNET OF THINGS JOURNAL (2022)

Article Computer Science, Information Systems

Delay-Optimized Resource Allocation in Fog-Based Vehicular Networks

Kecheng Zhang et al.

Summary: This study introduces a fog computing-based VNET that optimizes resource allocation to address transmission delays, achieving efficiency and reliability enhancements.

IEEE INTERNET OF THINGS JOURNAL (2021)

Article Engineering, Civil

Machine Learning-Based Workload Orchestrator for Vehicular Edge Computing

Cagatay Sonmez et al.

Summary: The vision of the Internet of Vehicles includes intelligent highway scenarios and self-driving vehicles, where a streamlined edge computing infrastructure is required for computational offloading. However, the highly dynamic environment presents challenges in efficiently operating a VEC system, making task allocation a crucial decision problem that conventional methods struggle with.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2021)

Article Engineering, Electrical & Electronic

Distributed Multi-Cloud Multi-Access Edge Computing by Multi-Agent Reinforcement Learning

Yutong Zhang et al.

Summary: This paper investigates a three-layer distributed multi-access edge computing network, where clouds, MEC servers, and edge devices are deployed at different layers to reduce system latency through task offloading and resource allocation. By proposing a distributed scheme based on reinforcement learning, a lower system latency is achieved compared to existing schemes, while also discussing the impact of resource competition and device numbers on performance.

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS (2021)

Article Engineering, Electrical & Electronic

Deep Reinforcement Learning Based Massive Access Management for Ultra-Reliable Low-Latency Communications

Helin Yang et al.

Summary: With the rapid deployment of Internet of Things (IoT), fifth-generation (5G) and beyond 5G networks are needed to support massive access of devices over limited radio spectrum. This paper proposes a joint energy-efficient subchannel assignment and power control approach to maximize network energy efficiency and meet different QoS requirements, while addressing reliability and latency constraints in the massive access scenario through distributed cooperative learning based on deep reinforcement learning.

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS (2021)

Article Computer Science, Information Systems

Joint task offloading and resource allocation in vehicle-assisted multi-access edge computing

Jianbin Xue et al.

Summary: The proposed vehicle-assisted MEC (VMEC) paradigm incentivizes intelligent vehicles with idle computation resources to provide computation offloading services, aiming to improve system utility.

COMPUTER COMMUNICATIONS (2021)

Article Engineering, Electrical & Electronic

Adaptive Computing Scheduling for Edge-Assisted Autonomous Driving

Mushu Li et al.

Summary: The paper investigates computing resource scheduling for real-time applications in autonomous driving, aiming to minimize the traveled distance of vehicles by finding a scheduling scheme for the edge server. The approach determines the processing order based on vehicle mobility and edge server computing capability. The proposed scheduling scheme delivers computing results promptly to vehicles while adapting to time-variant vehicle mobility.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2021)

Article Engineering, Electrical & Electronic

Self-Learning Based Computation Offloading for Internet of Vehicles: Model and Algorithm

Quyuan Luo et al.

Summary: In this paper, a self-learning based distributed computation offloading scheme for IoV is proposed, establishing a game-theoretic model for optimal offloading decision-making. Through extensive simulations, the scheme outperforms counterparts and achieves significant improvements in time and message overhead.

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS (2021)

Article Computer Science, Information Systems

Parking Edge Computing: Parked-Vehicle-Assisted Task Offloading for Urban VANETs

Chunmei Ma et al.

Summary: The research introduces the concept of parking edge computing, utilizing parked vehicle resources in urban areas to assist edge servers in task offloading. A task scheduling algorithm and local task scheduling policy are designed, along with a time-related trajectory prediction model to enhance task offloading performance.

IEEE INTERNET OF THINGS JOURNAL (2021)

Article Computer Science, Information Systems

Multiagent Deep Reinforcement Learning for Vehicular Computation Offloading in IoT

Xiaoyu Zhu et al.

Summary: This article explores the vehicular computation offloading problem in mobile-edge computing and proposes a multiagent deep reinforcement learning-based offloading scheme. The effectiveness and superiority of the proposed scheme are verified through a large number of simulations.

IEEE INTERNET OF THINGS JOURNAL (2021)

Article Computer Science, Information Systems

Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial

Amal Feriani et al.

Summary: Deep Reinforcement Learning (DRL) has made significant advances in solving sequential decision-making problems in various fields, especially in wireless communications. The tutorial focuses on the role of DRL with an emphasis on deep Multi-Agent Reinforcement Learning (MARL) in AI-enabled wireless networks. It presents mathematical frameworks for single-agent RL and MARL, emphasizes the application of RL beyond the model-free perspective, and highlights the potential applications in future wireless networks. The state-of-the-art of MARL in Mobile Edge Computing (MEC), Unmanned Aerial Vehicles (UAV) networks, and cell-free massive MIMO is overviewed, along with identifying promising future research directions.

IEEE COMMUNICATIONS SURVEYS AND TUTORIALS (2021)

Article Engineering, Multidisciplinary

Resource Allocation for Delay-Sensitive Vehicle-to-Multi-Edges (V2Es) Communications in Vehicular Networks: A Multi-Agent Deep Reinforcement Learning Approach

Jing Wu et al.

Summary: The V2Es communication framework in vehicular networks improves service quality by utilizing edge node resources and reduces service latency effectively through reinforcement learning method learning dynamic communication states.

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING (2021)

Article Engineering, Electrical & Electronic

Decentralized Configuration Protocols for Low-Cost Offloading From Multiple Edges to Multiple Vehicular Fogs

Li-Hsing Yen et al.

Summary: The paper introduces a two-tier federated Edge and Vehicular-Fog (EVF) system that can reduce costs through offloading configuration. Simulation results show that the proposed approach effectively leverages the heterogeneity between edge systems and VFs.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2021)

Article Engineering, Electrical & Electronic

Mobility-Aware Multi-User Offloading Optimization for Mobile Edge Computing

Wenhan Zhan et al.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2020)

Article Computer Science, Information Systems

Delay-Sensitive Task Offloading in the 802.11p-Based Vehicular Fog Computing Systems

Qiong Wu et al.

IEEE INTERNET OF THINGS JOURNAL (2020)

Article Computer Science, Information Systems

Deep-Reinforcement-Learning-Based Mode Selection and Resource Allocation for Cellular V2X Communications

Xinran Zhang et al.

IEEE INTERNET OF THINGS JOURNAL (2020)

Article Computer Science, Information Systems

An Infrastructure-Assisted Workload Scheduling for Computational Resources Exploitation in the Fog-Enabled Vehicular Network

Ibrahim Sorkhoh et al.

IEEE INTERNET OF THINGS JOURNAL (2020)

Article Computer Science, Information Systems

Deep-Reinforcement-Learning-Based Offloading Scheduling for Vehicular Edge Computing

Wenhan Zhan et al.

IEEE INTERNET OF THINGS JOURNAL (2020)

Article Engineering, Electrical & Electronic

Low-Delay Path Selection for Cluster-Based Buffer-Aided Vehicular Communications

Md Zahangir Alam et al.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2020)

Article Engineering, Electrical & Electronic

Priority-Aware Task Offloading in Vehicular Fog Computing Based on Deep Reinforcement Learning

Jinming Shi et al.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2020)

Article Computer Science, Information Systems

A Proactive Reliable Mechanism-Based Vehicular Fog Computing Network

Luobing Dong et al.

IEEE INTERNET OF THINGS JOURNAL (2020)

Article Computer Science, Information Systems

Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks

Liang Huang et al.

IEEE TRANSACTIONS ON MOBILE COMPUTING (2020)

Article Computer Science, Information Systems

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

Seung-seob Lee et al.

IEEE INTERNET OF THINGS JOURNAL (2020)

Article Computer Science, Information Systems

Advanced Deep Learning-Based Computational Offloading for Multilevel Vehicular Edge-Cloud Computing Networks

Mashael Khayyat et al.

IEEE ACCESS (2020)

Article Engineering, Electrical & Electronic

DEEP REINFORCEMENT LEARNING FOR MOBILE 5G AND BEYOND Fundamentals, Applications, and Challenges

Zehui Xiong et al.

IEEE VEHICULAR TECHNOLOGY MAGAZINE (2019)

Article Computer Science, Information Systems

Folo: Latency and Quality Optimized Task Allocation in Vehicular Fog Computing

Chao Zhu et al.

IEEE INTERNET OF THINGS JOURNAL (2019)

Article Engineering, Electrical & Electronic

Spectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning

Le Liang et al.

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS (2019)

Article Telecommunications

Deep Reinforcement Learning for Intelligent Internet of Vehicles: An Energy-Efficient Computational Offloading Scheme

Zhaolong Ning et al.

IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING (2019)

Article Computer Science, Information Systems

Offloading in Mobile Cloudlet Systems with Intermittent Connectivity

Yang Zhang et al.

IEEE TRANSACTIONS ON MOBILE COMPUTING (2015)