Computing resource allocation scheme of IOV using deep reinforcement learning in edge computing environment

Zhang, Yiwei; Zhang, Min; Fan, Chunju; Li, Fuqiang; Li, Baofang

doi:10.1186/s13634-021-00750-6

Cited by 21 publications

(6 citation statements)

References 32 publications

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“…In response to the issues associated with meta-heuristic algorithms, existing research has applied deep reinforcement learning techniques to vehicular edge computing. [19] proposed a computation resource allocation scheme based on a deep reinforcement learning network for vehicular edge computing scenarios. A task resource allocation model for vehicular networks is determined with service node computation capabilities and vehicle travel speeds as constraints.…”

Section: Related Workmentioning

confidence: 99%

Digital Twin-Assisted Vehicles Edge Network Computing Offloading Scheme

Zhu,

Tan,

2023

Preprint

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Traditional vehicle edge computing research has not fully considered the differences between vehicle tasks and edge server computing resources while often ignoring the use of deep reinforcement learning (DRL), which requires a large amount of training data, and DRL is easy to fall into the problem of local optimality. Therefore, this paper proposed a digital twin (DT) -assisted vehicle edge network computation offloading method. For the problem of DRL requiring a large amount of training data, a real-time data acquisition method based on DT is proposed for vehicle tasks and edge nodes. The differentiability of computing resources proposed a computing model based on deep neural network (DNN) partitioning and early exit mechanism, making full use of the computing resources of edge servers and vehicles, reducing the computation delay and transmission delay of tasks, Aiming at the problem that the asynchronous dominant actor-critic (A3C) algorithm in DRL is easy to fall into the local optimal solution, the dynamic baseline and ε-greedy policy are introduced based on A3C and used to select the channel to reduce the transmission delay further. Experimental results show that this scheme outperforms other comparative methods and can effectively reduce the delay of task offloading in vehicular edge computing.

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Section: Related Workmentioning

confidence: 99%

Digital Twin-Assisted Vehicles Edge Network Computing Offloading Scheme

Zhu,

Tan,

2023

Preprint

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“…Finally, the authors of [105] considered a specific use case, Internet of Vehicles (IoV), taking into account the specific constraints of this environment. Offloading solutions for computing tasks are more difficult due to the diversity of communication quality in the current IoV and the highspeed mobility of vehicles.…”

Section: E Othersmentioning

confidence: 99%

Non-Orthogonal Multiple Access for Offloading in Multi-Access Edge Computing: A Survey

Dulout,

Mendiboure,

Pousset

et al. 2023

IEEE Access

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Multi-Access Edge Computing (MEC) architectures now seem to represent the future of data processing architectures. Indeed, they have the potential to optimize the use of the backhaul network, guarantee the implementation of real-time applications and offer services adapted to the user's context. To further improve the performance of MEC architectures, it may be worth combining them with a new technology at the physical layer: Non Orthogonal Multiple Access (NOMA). During the task offloading process, this could enhance energy efficiency, maximize the number of users benefiting from MEC services and further reduce latency. That is why the article focuses on the use of NOMA for task offloading in MEC architectures and offers a comprehensive study on the subject. First, we define a taxonomy to ensure a systematic review of existing work. We then analyze and compare existing works, classifying them according to their purpose. Based on this, we then discuss the benefits and limitations of existing work, highlighting some good practices. Finally, we identify future research directions in the field of NOMAassisted MEC architectures.

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“…Here, comparison has been made between the Lagrange Dual Method [11] and the proposed scheme that is the DRL Model. Relation between the input and the optimization solution may be achieved using deep neural networks (DNNs) universal approximation capabilities.…”

Section: Throughputmentioning

confidence: 99%

“…Nonetheless, due to densification and with limited resources, it is difficult to schedule the resource to collect and process real-time requests from the vehicles, making it difficult to guarantee efficient and reliable data transmission by traditional IoV communications. Through resource sharing, it will be possible to increase the execution speed of a computing task and overcome the insufficient computing resource problem for the vehicle, thus providing ultra-low latency, high bandwidth, higher responsiveness, and throughput to the users [11,12]. In reality, the network status and available resources of RSU vary dynamically due to the mobility of vehicles.…”

Section: Introductionmentioning

confidence: 99%

Authentication and Resource Allocation Strategies during Handoff for 5G IoVs Using Deep Learning

Hemavathi¹,

Akhila²,

Alotaibi

et al. 2022

Energies

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One of the most sought-after applications of cellular technology is transforming a vehicle into a device that can connect with the outside world, similar to smartphones. This connectivity is changing the automotive world. With the speedy growth and densification of vehicles in Internet of Vehicles (IoV) technology, the need for consistency in communication amongst vehicles becomes more significant. This technology needs to be scalable, secure, and flexible when connecting products and services. 5G technology, with its incredible speed, is expected to power the future of vehicular networks. Owing to high mobility and constant change in the topology, cooperative intelligent transport systems ensure real time connectivity between vehicles. For ensuring a seamless connectivity amongst the entities in vehicular networks, a significant alternative to design is support of handoff. This paper proposes a scheme for the best Road Side Unit (RSU) selection during handoff. Authentication and security of the vehicles are ensured using the Deep Sparse Stacked Autoencoder Network (DS2AN) algorithm, developed using a deep learning model. Once authenticated, resource allocation by RSU to the vehicle is accomplished through Deep-Q learning (DQL) techniques. Compared with the existing handoff schemes, Reinforcement Learning based on the MDP (RL-MDP) has been found to have a 13% lesser decision delay for selecting the best RSU. A higher level of security and minimum time requirement for authentication is achieved using DS2AN. The proposed system simulation results demonstrate that it ensures reliable packet delivery, significantly improving system throughput, upholding tolerable delay levels during a change of RSUs.

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Computing resource allocation scheme of IOV using deep reinforcement learning in edge computing environment

Cited by 21 publications

References 32 publications

Digital Twin-Assisted Vehicles Edge Network Computing Offloading Scheme

Digital Twin-Assisted Vehicles Edge Network Computing Offloading Scheme

Non-Orthogonal Multiple Access for Offloading in Multi-Access Edge Computing: A Survey

Authentication and Resource Allocation Strategies during Handoff for 5G IoVs Using Deep Learning

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