Resource Allocation in DT-Assisted Internet of Vehicles via Edge Intelligent Cooperation

Liu, Tong; Tang, Lun; Wang, Weili; He, Xiaoqiang; Chen, Qianbin; Zeng, Xiaoping; Jiang, Haitao

doi:10.1109/jiot.2022.3156100

Cited by 42 publications

(5 citation statements)

References 52 publications

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“…However, efficiently allocating such resources using machine learning techniques and artificial intelligence requires a large amount of training data and high computing power that is impossible to achieve in a resource-constrained onboard unit or a roadside unit. Liu et al [113] proposed a DT-enabled intelligent edge cooperation scheme, enabling optimal resource allocation and intelligent edge cooperation. The proposal aims to minimize the delay to meet the requirements of time-sensitive applications in ITS.…”

Section: Internet Of Vehiclesmentioning

confidence: 99%

Sustainable Intelligent Transportation Systems via Digital Twins: A Contextualized Survey

Martínez,

Campelo,

Ribeiro

2024

Preprint

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Section: Internet Of Vehiclesmentioning

confidence: 99%

Sustainable Intelligent Transportation Systems via Digital Twins: A Contextualized Survey

Martínez,

Campelo,

Ribeiro

2024

Preprint

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“…This is another important potential research area, focused on developing efficient resource optimization and allocation strategies for XR coupled with DT in resource-constrained edge/cloud environments [71]. This involves exploring techniques to effectively utilize computing resources, network bandwidth, minimize latency, reduce energy consumption, and storage capacity to ensure seamless XR experiences whereas minimizing resource consumption [30].…”

Section: ) Efficient Resource Managementmentioning

confidence: 99%

“…Advancements in deep learning, reinforcement learning, federated learning and natural language processing are expected to drive innovations in various domains [69], [70]. AI-powered applications such as autonomous vehicles, virtual, DT assistants, and predictive analytics are anticipated to become more prevalent in the coming years [71,72].…”

Section: ) 5g/6g Networkmentioning

confidence: 99%

Resource-Constrained EXtended Reality Operated With Digital Twin in Industrial Internet of Things

Kamdjou,

Baudry,

Havard

et al. 2024

IEEE Open J. Commun. Soc.

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EXtended Reality (XR) alongside the Digital Twin (DT) in Industrial Internet of Things (IIoT) emerges as a promising next-generation technology. Its diverse applications hod the potential to revolutionize multiple facets of Industry 4.0 and serve as a cornerstone for the rise of Industry 5.0. However, current systems are still not effective in providing a high-quality experience for users due to various factors, one of which is their limited resources for processing and transmitting complex data and big data. To overcome these challenges, this paper presents an in-depth analysis of performance optimization techniques for resource-constrained Augmented Reality (AR) and/or Virtual Reality (VR) environments operating with DT, with a specific focus on Quality of Service (QoS), Quality of Experience (QoE), Edge-Cloud architectures and future research directions. Furthermore, this study delves into the intricate complex trade-off relationships involving optimization factors, including system quality, information quality, and QoE. In addition, it also explores potential solutions based on powerful emerging technological tools, including data compression, blockchain, cloud computing, quantum computing, Artificial Intelligence (AI) / Machine Learning (ML), and cybersecurity in the Cyber-Physical Systems (CPS). The insights provided in this comprehensive survey can inspire and guide researchers and industrial practitioners in optimizing performance for XR with DT applications in resource-constrained Smart Manufacturing System (SMS).

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“…The authors in [24] present a holistic network virtualization model that integrates DT and network slicing, in which an environment-aware offloading method is designed to reduce the total time of the system. Liu et al [25] propose a DT-assisted edge intelligent collaboration scheme in IoV to realize optimal 3C resources allocation and edge collaboration. Considering the deviations between the physical world and the digital world, they utilize a DRL algorithm to obtain the optimal offloading strategy and diminish the response delay.…”

Section: Related Workmentioning

confidence: 99%

Blockchain Enabled Task Offloading Based on Edge Cooperation in the Digital Twin Vehicular Edge Network

Chen

et al. 2023

Preprint

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The rapid development of the Internet of Vehicles (IoV) along with the emergence of intelligent applications have put forward higher requirements for massive task offloading. Even though Mobile Edge Computing (MEC) can diminish network transmission delay and ease network congestion, the constrained heterogeneous resources of a single edge server and the highly dynamic topology of vehicular edge networks may compromise the efficiency of task offloading, including latency and energy consumption. Vehicular edge networks are also vulnerable to malicious outside attacks. In this paper, we propose a new blockchain-enabled digital twin vehicular edge network (DTVEN) where digital twin (DT) is exploited to monitor network communication, computation, and caching (3C) resources management in real time to provide rich data for offloading decision-making, as well as blockchain is utilized to secure fair and decentralized offloading transactions among DTs. To ensure 3C resources sharing across edge servers, we design a DT-assisted edge cooperation scheme, which makes full use of edge resources in vehicular networks. Furthermore, a DT-based smart contract is built to achieve a quick and effective consensus process. Then, we apply a task offloading algorithm based on an improved cuckoo algorithm (ICA) and a resource allocation scheme based on greedy strategy to minimize network cost by comprehensively taking into account latency and energy consumption. Numerical results demonstrate that our proposed scheme outperforms the existing schemes in terms of network cost.

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Resource Allocation in DT-Assisted Internet of Vehicles via Edge Intelligent Cooperation

Cited by 42 publications

References 52 publications

Sustainable Intelligent Transportation Systems via Digital Twins: A Contextualized Survey

Sustainable Intelligent Transportation Systems via Digital Twins: A Contextualized Survey

Resource-Constrained EXtended Reality Operated With Digital Twin in Industrial Internet of Things

Blockchain Enabled Task Offloading Based on Edge Cooperation in the Digital Twin Vehicular Edge Network

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