Deep-Reinforcement-Learning-Based Distributed Computation Offloading in Vehicular Edge Computing Networks

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Summary: This article investigates the problem of task partitioning and power control in a fog computing network and proposes a MADDPG-based task offloading algorithm for MDs to maximize system utility and reduce energy consumption.

IEEE INTERNET OF THINGS JOURNAL (2022)

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Article Computer Science, Information Systems

Deep Reinforcement Learning for Energy-Efficient Computation Offloading in Mobile-Edge Computing

Huan Zhou et al.

Summary: This article investigates the joint optimization of computation offloading and resource allocation in a dynamic multiuser Mobile-edge computing (MEC) system. The objective is to minimize the energy consumption of the entire MEC system by considering the delay constraint and uncertain resource requirements of heterogeneous computation tasks. The proposed reinforcement learning method and double deep Q network-based method outperform other baseline methods in different scenarios.

IEEE INTERNET OF THINGS JOURNAL (2022)

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

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Article Computer Science, Information Systems

Adaptive Computation Offloading Policy for Multi-Access Edge Computing in Heterogeneous Wireless Networks

Hongchang Ke et al.

Summary: This article discusses how to manage a large number of computation tasks generated by mobile terminals in heterogeneous wireless networks through the combination of edge computing and unmanned aerial vehicles (UAVs). Two distributed computation offloading schemes based on deep reinforcement learning are proposed to minimize latency and energy consumption.

IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT (2022)

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Lyapunov-Based Partial Computation Offloading for Multiple Mobile Devices Enabled by Harvested Energy in MEC

Min Guo et al.

Summary: Mobile-edge computing (MEC) has gained significant attention for processing computation tasks nearby mobile devices. This study investigates partial computation offloading schemes enabled by harvested energy for multiple mobile devices in MEC, and proposes an algorithm based on Lyapunov optimization (LOMUCO) to achieve optimal solution. Experimental results demonstrate the superiority of LOMUCO in terms of energy consumption and computation task delay.

IEEE INTERNET OF THINGS JOURNAL (2022)

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Article Automation & Control Systems

DRL-Based Resource Allocation for Computation Offloading in IoV Networks

Bishmita Hazarika et al.

Summary: Efficient real-time resource allocation in a dynamic vehicular fog computing environment is challenging. This article proposes a priority-sensitive task offloading and resource allocation scheme, and designs corresponding deep reinforcement learning and deep deterministic policy gradient algorithms. The feasibility of the proposed scheme is validated through extensive numerical experiments.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2022)

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A new task offloading algorithm in edge computing

Zhenjiang Zhang et al.

Summary: The Internet of Things is changing the world, and edge computing is an application that can help reduce user latency, enhance user experience, and balance resource utilization. Through deep reinforcement learning algorithms, reasonable task allocation can be achieved to improve system adaptability and stability.

EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING (2021)

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Energy-Efficient D2D-Assisted Computation Offloading in NOMA-Enabled Cognitive Networks

Yuxia Cheng et al.

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2021)

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Soft Actor-Critic DRL for Live Transcoding and Streaming in Vehicular Fog-Computing-Enabled IoV

Fang Fu et al.

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IEEE INTERNET OF THINGS JOURNAL (2021)

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Article Computer Science, Information Systems

Heterogeneous Task Offloading and Resource Allocations via Deep Recurrent Reinforcement Learning in Partial Observable Multifog Networks

Jungyeon Baek et al.

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IEEE INTERNET OF THINGS JOURNAL (2021)

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Article Computer Science, Information Systems

Task Offloading Strategy and Simulation Platform Construction in Multi-User Edge Computing Scenario

Guilu Wu et al.

Summary: This paper proposes a multi-user task offloading strategy based on game theory, utilizing mobile edge computing technology to solve the issue of insufficient computing resources in vehicular networks, effectively reducing system overhead.

ELECTRONICS (2021)

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IEEE TRANSACTIONS ON COMMUNICATIONS (2021)

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

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An Energy Aware Offloading Scheme for Interdependent Applications in Software-Defined IoV With Fog Computing Architecture

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2021)

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

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS (2021)

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A Hybrid DQN and Optimization Approach for Strategy and Resource Allocation in MEC Networks

Yi-Chen Wu et al.

Summary: This study proposes two hybrid approaches to tackle the offloading decision and resource allocation problem in a multi-user multi-server mobile edge computing network, optimizing at user equipment and computational access point, simulation results show that the hybrid approaches outperform baseline algorithms and pure-DQN approach significantly.

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS (2021)

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A Deep Reinforcement Learning Based Offloading Game in Edge Computing

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IEEE TRANSACTIONS ON COMPUTERS (2020)

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Blockchain Empowered Asynchronous Federated Learning for Secure Data Sharing in Internet of Vehicles

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Deep Reinforcement Learning-Based Adaptive Computation Offloading for MEC in Heterogeneous Vehicular Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2020)

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Offloading and Resource Allocation With General Task Graph in Mobile Edge Computing: A Deep Reinforcement Learning Approach

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS (2020)

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

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IEEE INTERNET OF THINGS JOURNAL (2020)

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Adaptive Task Offloading in Vehicular Edge Computing Networks: a Reinforcement Learning Based Scheme

Jie Zhang et al.

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Reinforcement Learning-Based Mobile Offloading for Edge Computing Against Jamming and Interference

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IEEE TRANSACTIONS ON COMMUNICATIONS (2020)

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

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2020)

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Proceedings Paper Computer Science, Artificial Intelligence

Joint Task Offloading and Resource Allocation for Mobile Edge Computing in Ultra-Dense Network

Zhipeng Cheng et al.

2020 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM) (2020)

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Edge QoE: Computation Offloading With Deep Reinforcement Learning for Internet of Things

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Semi-Online Computational Offloading by Dueling Deep-Q Network for User Behavior Prediction

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IEEE ACCESS (2020)

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Dynamic Computation Offloading for Mobile Cloud Computing: A Stochastic Game-Theoretic Approach

Jianchao Zheng et al.

IEEE TRANSACTIONS ON MOBILE COMPUTING (2019)

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Multi-Antenna NOMA for Computation Offloading in Multiuser Mobile Edge Computing Systems

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IEEE TRANSACTIONS ON COMMUNICATIONS (2019)

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Learning-Based Privacy-Aware Offloading for Healthcare IoT With Energy Harvesting

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IEEE INTERNET OF THINGS JOURNAL (2019)

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Computation Offloading and Resource Allocation For Cloud Assisted Mobile Edge Computing in Vehicular Networks

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Multi-Hop Cooperative Computation Offloading for Industrial IoT-Edge-Cloud Computing Environments

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A Survey on Mobile Edge Computing: The Communication Perspective

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Deep-Reinforcement-Learning-Based Distributed Computation Offloading in Vehicular Edge Computing Networks

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