Collaborative Vehicular Edge Computing Towards Greener ITS

Buda, Su; Guleng, Siri; Wu, Celimuge; Zhang, Jie-Fang; Yau, Kok‐Lim Alvin; Ji, Yusheng

doi:10.1109/access.2020.2985731

Cited by 19 publications

(10 citation statements)

References 27 publications

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“…Collaborative vehicular edge computing (CVEC) has been studied in many articles covering collaborative coalition formation [22], collaborative computation offloading between vehicles [23], volunteer assisted collaborative offloading [24], and collaborative route selection [25]. K. Wang et al [26] consider using edge servers with both horizontal and vertical collaborative offloading to support vehicular services in CVEC.…”

Section: B Collaborative Vehicular Edge Computingmentioning

confidence: 99%

Virtual Edge: Exploring Computation Offloading in Collaborative Vehicular Edge Computing

Cha

Yoshinaga

et al. 2021

IEEE Access

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Vehicular edge computing (VEC) has been a new paradigm to support computation-intensive and latency-sensitive services. However, the scarcity of computational resources is still a challenge. Making efficient use of sporadic idle computational resources on smart vehicles in the vicinity to extend the resource capability of each vehicle is an important research issue. In this paper, we propose Virtual Edge, which is an efficient scheme to utilize free computational resources of multiple vehicles as a virtual server to facilitate collaborative vehicular edge computing. We design a virtual edge formation algorithm that considers both the stability of virtual edge and the computational resources available at the vehicles constituting the virtual edge. The prediction of the link duration between vehicles reduces the number of computation offloading failures caused by unexpected link disconnections. Extensive simulations with realistic vehicle movements are conducted to show the advantage of the proposed scheme over existing baselines in terms of the completion ratio of computation offloading tasks and average task execution time. INDEX TERMS Collaborative vehicular edge computing, virtual edge, computation offloading

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Section: B Collaborative Vehicular Edge Computingmentioning

confidence: 99%

Virtual Edge: Exploring Computation Offloading in Collaborative Vehicular Edge Computing

Cha

Yoshinaga

et al. 2021

IEEE Access

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“…Resource management approaches in vehicle cloud computing (VCC) [40], vehicle fog computing (VFC) [41], and vehicle edge computing (VEC) [42] are illustrated in Figure 7, which have been significantly studied to guarantee the desirable latency 1 ms. The recent advanced techniques of computation power and spectrum allocation are analyzed in cloud, fog, and edge computing via traditional optimization approaches, ML-based approaches, and DRL-assisted approaches.…”

Section: Computation Power and Resource Allocation Techniques For The 5g Vehicular Networkmentioning

confidence: 99%

DRL‐Based Intelligent Resource Allocation for Diverse QoS in 5G and toward 6G Vehicular Networks: A Comprehensive Survey

Nguyen

et al. 2021

Wireless Communications and Mobile Computing

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The vehicular network is taking great attention from both academia and industry to enable the intelligent transportation system (ITS), autonomous driving, and smart cities. The system provides extremely dynamic features due to the fast mobile characteristics. While the number of different applications in the vehicular network is growing fast, the quality of service (QoS) in the 5G vehicular network becomes diverse. One of the most stringent requirements in the vehicular network is a safety-critical real-time system. To guarantee low-latency and other diverse QoS requirements, wireless network resources should be effectively utilized and allocated among vehicles, such as computation power in cloud, fog, and edge servers; spectrum at roadside units (RSUs); and base stations (BSs). Historically, optimization problems have mostly been investigated to formulate resource allocation and are solved by mathematical computation methods. However, the optimization problems are usually nonconvex and hard to be solved. Recently, machine learning (ML) is a powerful technique to cope with the complexity in computation and has capability to cope with big data and data analysis in the heterogeneous vehicular network. In this paper, an overview of resource allocation in the 5G vehicular network is represented with the support of traditional optimization and advanced ML approaches, especially a deep reinforcement learning (DRL) method. In addition, a federated deep reinforcement learning- (FDRL-) based vehicular communication is proposed. The challenges, open issues, and future research directions for 5G and toward 6G vehicular networks, are discussed. A multiaccess edge computing assisted by network slicing and a distributed federated learning (FL) technique is analyzed. A FDRL-based UAV-assisted vehicular communication is discussed to point out the future research directions for the networks.

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“…The study of the networking performance of blockchain in vehicular IoT environments is an urgent and under-explored topic. More effort can be made to improve the networking performance of blockchain in vehicular environments by combining with the networking [ 116 ] and computing technologies [ 117 ], as well as decentralized consensus algorithms.…”

Section: Future Research Directionsmentioning

confidence: 99%

Blockchain for Vehicular Internet of Things: Recent Advances and Open Issues

Peng

Gao

et al. 2020

Sensors

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The vehicular Internet of Things (IoT) comprises enabling technologies for a large number of important applications including collaborative autonomous driving and advanced transportation systems. Due to the mobility of vehicles, strict application requirements, and limited communication resources, the conventional centralized control fails to provide sufficient quality of service for connected vehicles, so a decentralized approach is required in the vicinity to satisfy the requirements of delay-sensitive and mission-critical applications. A decentralized system is also more resistant to the single point of failure problem and malicious attacks. Blockchain technology has been attracting great interest due to its capability of achieving a decentralized, transparent, and tamper-resistant system. There are many studies focusing on the use of blockchain in managing data and transactions in vehicular environments. However, the application of blockchain in vehicular environments also faces some technical challenges. In this paper, we first explain the fundamentals of blockchain and vehicular IoT. Then, we conduct a literature review on the existing research efforts of the blockchain for vehicular IoT by discussing the research problems and technical issues. After that, we point out some future research issues considering the characteristics of both blockchain and vehicular IoT.

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Collaborative Vehicular Edge Computing Towards Greener ITS

Cited by 19 publications

References 27 publications

Virtual Edge: Exploring Computation Offloading in Collaborative Vehicular Edge Computing

Virtual Edge: Exploring Computation Offloading in Collaborative Vehicular Edge Computing

DRL‐Based Intelligent Resource Allocation for Diverse QoS in 5G and toward 6G Vehicular Networks: A Comprehensive Survey

Blockchain for Vehicular Internet of Things: Recent Advances and Open Issues

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