E-Auto: A Communication Scheme for Connected Vehicles with Edge-Assisted Autonomous Driving

Wang, Haoxin; Kim, BaekGyu; Xie, Jiang; Han, Zhu

doi:10.1109/icc.2019.8761551

Cited by 20 publications

(8 citation statements)

References 17 publications

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“…2) VANETs that use cloud services, where the RSUs act as gateways to the internet. By providing fog layers, this technology can be made even more effective for vehicular networks [165]- [167]. Role of other technology in the network [63] Cloud server was used as part of the blockchain center for creating, verifying and adding the blocks.…”

Section: A Cloud and Fog Computingmentioning

confidence: 99%

A Comprehensive Survey on the Applications of Blockchain for Securing Vehicular Networks

Alladi¹,

Chamola²,

Sahu³

et al. 2022

Preprint

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Vehicular networks promise features such as traffic management, route scheduling, data exchange, entertainment, and much more. With any large-scale technological integration comes the challenge of providing security. Blockchain technology has been a popular choice of many studies for making the vehicular network more secure. Its characteristics meet some of the essential security requirements such as decentralization, transparency, tamper-proof nature, and public audit. This study catalogues some of the notable efforts in this direction over the last few years. We analyze around 75 blockchain-based security schemes for vehicular networks from an application, security, and blockchain perspective. The application perspective focuses on various applications which use secure blockchainbased vehicular networks such as transportation, parking, data sharing/ trading, and resource sharing. The security perspective focuses on security requirements and attacks. The blockchain perspective focuses on blockchain platforms, blockchain types, and consensus mechanisms used in blockchain implementation. We also compile the popular simulation tools used for simulating blockchain and for simulating vehicular networks. Additionally, to give the readers a broader perspective of the research area, we discuss the role of various state-of-the-art emerging technologies in blockchain-based vehicular networks. Lastly, we summarize the survey by listing out some common challenges and the future research directions in this field.

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Section: A Cloud and Fog Computingmentioning

confidence: 99%

A Comprehensive Survey on the Applications of Blockchain for Securing Vehicular Networks

Alladi¹,

Chamola²,

Sahu³

et al. 2022

Preprint

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“…Most existing research on computation offloading focuses on how to make offloading decisions. [38]- [41] coordinate the scheduling of offloading requests for multiple applications to further reduce the wireless energy cost caused by the long tail problem. [42] proposes an energy-efficient offloading approach for multicore-based mobile devices.…”

Section: Related Workmentioning

confidence: 99%

Energy Drain of the Object Detection Processing Pipeline for Mobile Devices: Analysis and Implications

Wang

Kim

Xie

et al. 2021

IEEE Trans. on Green Commun. Netw.

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Applying deep learning to object detection provides the capability to accurately detect and classify complex objects in the real world. However, currently, few mobile applications use deep learning because such technology is computationintensive and energy-consuming. This paper, to the best of our knowledge, presents the first detailed experimental study of a mobile augmented reality (AR) client's energy consumption and the detection latency of executing Convolutional Neural Networks (CNN) based object detection, either locally on the smartphone or remotely on an edge server. In order to accurately measure the energy consumption on the smartphone and obtain the breakdown of energy consumed by each phase of the object detection processing pipeline, we propose a new measurement strategy. Our detailed measurements refine the energy analysis of mobile AR clients and reveal several interesting perspectives regarding the energy consumption of executing CNN-based object detection. Furthermore, several insights and research opportunities are proposed based on our experimental results. These findings from our experimental study will guide the design of energy-efficient processing pipeline of CNN-based object detection.

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“…The computing architectures considered in papers [23], [132], [141]- [159], [177], [179]- [193] for supporting CVs are computation-aided architectures, where CVs only generate computing tasks (i.e., CVs are considered as computation sources only), leaving task computation to external units, such as nearby edge nodes (e.g., WiFi routers, small-cell BSs, and macro-cell BSs) or cloud servers, which depends on the service requirements on the computation load. Since CVs in computation-aided computing architectures do not possess the computation ability, their generated tasks must be offloaded to external computing infrastructures.…”

Section: A Computation-aided Computing Architecturesmentioning

confidence: 99%

“…2) Hybrid Architecture [23], [148]- [159], [177], [185]- [190]: Papers [148], [149] proposed a four-layer architecture for urban traffic management with the convergence of 5G networks, VANETs, MEC, and SDNs technologies, as shown in Fig. 5.…”

Section: Hybridmentioning

confidence: 99%

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Architectural Design Alternatives Based on Cloud/Edge/Fog Computing for Connected Vehicles

Wang

Liu

Kim

et al. 2020

IEEE Commun. Surv. Tutorials

Self Cite

113

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As vehicles playing an increasingly important role in people's daily life, requirements on safer and more comfortable driving experience have arisen. Connected vehicles (CVs) can provide enabling technologies to realize these requirements and have attracted widespread attentions from both academia and industry. These requirements ask for a well-designed computing architecture to support the Quality-of-Service (QoS) of CV applications. Computation offloading techniques, such as cloud, edge, and fog computing, can help CVs process computationintensive and large-scale computing tasks. Additionally, different cloud/edge/fog computing architectures are suitable for supporting different types of CV applications with highly different QoS requirements, which demonstrates the importance of the computing architecture design. However, most of the existing surveys on cloud/edge/fog computing for CVs overlook the computing architecture design, where they (i) only focus on one specific computing architecture and (ii) lack discussions on benefits, research challenges, and system requirements of different architectural alternatives. In this paper, we provide a comprehensive survey on different architectural design alternatives based on cloud/edge/fog computing for CVs. The contributions of this paper are: (i) providing a comprehensive literature survey on existing proposed architectural design alternatives based on cloud/edge/fog computing for CVs, (ii) proposing a new classification of computing architectures based on cloud/edge/fog computing for CVs: computation-aided and computation-enabled architectures, (iii) presenting a holistic comparison among different cloud/edge/fog computing architectures for CVs based on functional requirements of CV systems, including advantages, disadvantages, and research challenges, (iv) presenting a holistic overview on the design of CV systems from both academia and industry perspectives, including activities in industry, functional requirements, service requirements, and design considerations, and (v) proposing several open research issues of designing cloud/edge/fog computing architectures for CVs.

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E-Auto: A Communication Scheme for Connected Vehicles with Edge-Assisted Autonomous Driving

Cited by 20 publications

References 17 publications

A Comprehensive Survey on the Applications of Blockchain for Securing Vehicular Networks

A Comprehensive Survey on the Applications of Blockchain for Securing Vehicular Networks

Energy Drain of the Object Detection Processing Pipeline for Mobile Devices: Analysis and Implications

Architectural Design Alternatives Based on Cloud/Edge/Fog Computing for Connected Vehicles

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