An Energy Aware Algorithm for Edge Task Offloading

Xiong, Ao; Meng, Chen; Guo, Shaoyong; Li, Yongjie; Zhao, Yujing; Ou, Qinghai; Liu, Chuan; Xu, Siwen; Liu, Xiangang

doi:10.32604/iasc.2022.018881

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…MEC servers can provide flexible computing and storage resources for various terminals at the edge of the network. Many pieces of literature [15][16][17] have proposed various solutions to solve the problem of computing offload in mobile edge computing. Feng et al [3] proposed an edge computing framework for autonomous vehicles.…”

Section: Computing Offloading Methods Based On Mobile Edge Computingmentioning

confidence: 99%

A Trusted Edge Resource Allocation Framework for Internet of Vehicles

Zhong,

Xu,

Liao

et al. 2023

CMC

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With the continuous progress of information technique, assisted driving technology has become an effective technique to avoid traffic accidents. Due to the complex road conditions and the threat of vehicle information being attacked and tampered with, it is difficult to ensure information security. This paper uses blockchain to ensure the safety of driving information and introduces mobile edge computing technology to monitor vehicle information and road condition information in real time, calculate the appropriate speed, and plan a reasonable driving route for the driver. To solve these problems, this paper proposes a trusted edge resource allocation framework for assisted driving service, which includes two stages: the blockchain generation stage (the first stage) and assisted driving service stage (the second stage). Furthermore, in the first stage, a delay-and-throughput-oriented block generation model for the mobile terminal is designed. In the second stage, a balanced offloading algorithm for assisted driving service based on edge collaboration is proposed to solve the problems of unbalanced load of cluster mobile edge computing (MEC) servers and low resource utilization of the system. And this paper optimizes the throughput of blockchain and delay of the transportation network through deep reinforcement learning (DRL) algorithm. Finally, compared with joint computation and communication resources' allocation (JCCR) and resource allocation method based on binary offloading (RAB), our proposed scheme can optimize the delay by 7.4% and 26.7%, and support various application services of the vehicular networks more effectively.

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Section: Computing Offloading Methods Based On Mobile Edge Computingmentioning

confidence: 99%

A Trusted Edge Resource Allocation Framework for Internet of Vehicles

Zhong,

Xu,

Liao

et al. 2023

CMC

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“…The proposed joint optimization algorithm, JORA‐MADDPG, uses the multi‐agent deep deterministic policy gradient (DDPG) technique to maximize the utility of the vehicle. In order to reduce the amount of energy used by EdSrs, Xiong et al 112 developed a TO approach based on RL. The authors evaluated the present environment's condition, chose suitable deployment sites for edge tasks, and prioritized ECp optimization.…”

Section: Energy‐based Co Techniques In Ecmentioning

confidence: 99%

Computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication

Kanupriya,

Chana,

Goyal

2024

Concurrency and Computation

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SummaryIn today's era, Internet of Things (IoT) devices generate a vast amount of data, which is typically stored in the cloud environment and can be accessed by edge and IoT devices. The data generated by these devices are offloaded through computation offloading (CO) techniques in an edge/cloud computing environment. This paper conducts a systematic literature review (SLR) to review the state‐of‐the‐art CO techniques in edge computing (EC) in the context of energy, Quality of Service (QoS), authentication and traceability. In this SLR, the evolution of offloading techniques is analyzed in detail. A total of 138 articles, spanning from 2016 to 2023 (till date), have been classified into QoS, energy, and authentication and traceability‐based CO techniques. The optimization‐based techniques are the most preferred choices to improve the QoS and reduce energy in the research field of CO in EC. In addition, this paper explores the significant issues and challenges that require further investigation. For future research, energy, QoS, and data provenance for dependent or dynamic task offloading in mobility scenarios can be explored further.

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Large-Scale Mobile Edge Computing with Joint Offloading Decision and Resource Allocation

Luo

Wang

2022

Advances in Artificial Intelligence and Security

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An Energy Aware Algorithm for Edge Task Offloading

Cited by 6 publications

References 14 publications

A Trusted Edge Resource Allocation Framework for Internet of Vehicles

A Trusted Edge Resource Allocation Framework for Internet of Vehicles

Computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication

Large-Scale Mobile Edge Computing with Joint Offloading Decision and Resource Allocation

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