An Optimization Scheme for Task Offloading and Resource Allocation in Vehicle Edge Networks

Xu, Yuxin; Jin, Zilong; Zhang, Xiaorui; Zhang, Lejun

doi:10.32604/jiot.2020.011792

Cited by 4 publications

(2 citation statements)

References 12 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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“…, N} means that controllers offload the tasks to MEC servers, and O m, j (t) = N + 1 denotes that the tasks are offloaded to cloud computing servers. To enhance the security of the offloading process, we embed a blockchain system in the MEC layer (i.e., some MEC servers act as blockchain node and form a blockchain system) [32][33][34].…”

Section: System Architecturementioning

confidence: 99%

Optimization Scheme of Trusted Task Offloading in IIoT Scenario Based爋n燚QN

Wang¹,

Lu²,

Sun³

et al. 2023

Computers, Materials &Amp; Continua

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With the development of the Industrial Internet of Things (IIoT), end devices (EDs) are equipped with more functions to capture information. Therefore, a large amount of data is generated at the edge of the network and needs to be processed. However, no matter whether these computing tasks are offloaded to traditional central clusters or mobile edge computing (MEC) devices, the data is short of security and may be changed during transmission. In view of this challenge, this paper proposes a trusted task offloading optimization scheme that can offer low latency and high bandwidth services for IIoT with data security. Blockchain technology is adopted to ensure data consistency. Meanwhile, to reduce the impact of low throughput of blockchain on task offloading performance, we design the processes of consensus and offloading as a Markov decision process (MDP) by defining states, actions, and rewards. Deep reinforcement learning (DRL) algorithm is introduced to dynamically select offloading actions. To accelerate the optimization, we design a novel reward function for the DRL algorithm according to the scale and computational complexity of the task. Experiments demonstrate that compared with methods without optimization, our mechanism performs better when it comes to the number of task offloading and throughput of blockchain.

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