Demonstration of Mobile Edge Cloud for 5G Connected Cars

Zhdanenko, Oleksandr; Liu, Jianhui; Torre, Roberto; Mudriievskiy, Stanislav; Salah, Hani; Nguyen, Giang T.; Fitzek, H. P. Frank

doi:10.1109/ccnc.2019.8651783

Cited by 12 publications

(5 citation statements)

References 1 publication

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“…Most advanced applications of CVs and autonomous vehicles require significant amounts of data processing, caching and storage, which should be instantly available on the fly to ensure a comfortable and safe driving experience while avoiding traffic accidents. Although critical data are processed locally, each CV requires additional data processing at neighbouring edge servers, which can solve more complex but less urgent tasks [7]. Therefore, MEC is seen as a key enabler of edge intelligence and low-latency computing capabilities for CVs.…”

Section: A Mec In a Vehicular Environmentmentioning

confidence: 99%

Blockchain-based Route Selection with Allocation of Radio and Computing Resources for Connected Vehicles

Gazda¹,

Maksymyuk²,

Bečvář³

et al. 2023

Preprint

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<p>Most of the existing solutions for communication of connected vehicles (CVs) are focused on the optimization of resource allocation, however, not taking the driving routes of the CVs into account. In addition, no existing work considers the offloading of computing tasks from CVs to edge computing servers. In this paper, we introduce joint vehicular route selection and radio and computing resource allocation for CVs. The proposed algorithm minimizes the ratio of failed tasks along the entire vehicular route considering the availability of both radio and computing resources. To manage the allocation of resources among multiple CVs for each vehicular route, we develop a blockchain-based framework allowing resource reservation by means of nonfungible tokens (NFTs). Each NFT represents an exclusive right to the required amount of radio and computing resources for the given road segment and defined time interval. Simulation results show that the proposed vehicular route selec-<br> tion algorithm reduces the ratio of tasks not completed before deadline by up to 69% compared to the existing state-of-the-art algorithms.</p>

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Section: A Mec In a Vehicular Environmentmentioning

confidence: 99%

Blockchain-based Route Selection with Allocation of Radio and Computing Resources for Connected Vehicles

Gazda¹,

Maksymyuk²,

Bečvář³

et al. 2023

Preprint

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“…In addition to properly estimating user trajectories to guide content migration, a lot of research has been done on the policies and algorithms for server migration and selection. An interesting comparison is shown in [27]. In addition to assessing the need for service migration in a vehicular environment, different metrics used for server selection are compared, namely distance-based MEC, load-based MEC and their combination.…”

Section: Background Materialsmentioning

confidence: 99%

An edge abstraction layer enabling federated and hierarchical orchestration of CCAM services in 5G and beyond networks

Femminella¹,

Reali²

2022

ITU J-FET

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This paper shows a flexible orchestration solution for deploying Cooperative, Connected, and Automated Mobility (CCAM) services in 5G and beyond networks. This solution is based on the concepts of federation and hierarchy of orchestration functions. The federated approach is leveraged to cope with the differentiated complexity operation when multiple network operators are considered, whereas the hierarchical approach addresses the issue of jointly orchestrating multiple edge platforms in the network of a single operator. In this complex orchestration architecture, the main contribution of this paper consists of the design and implementation of an Abstraction and Adaptation Layer (AAL) for edge clouds, a new component enabling a truly cooperative and coordinated orchestration between different edge systems, characterized by appreciable experimental performance in terms of latency.

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“…Today, there are cars that connect to the internet using a SIM card, and applications [20] that require fast communication are being designed to use the benefits of the 5G [21,22] and IoT technology [23,24], creating an internet of vehicles digital space [25,26]. There are also other ways and protocols [27] that cars use to communicate between them, such as through blockchain secured ad-hoc vehicle networks [28,29] and LiFi communication [30,31].…”

Section: Connected Cars and Smart Transport Infrastructurementioning

confidence: 99%

Ubiquitous Computing: Driving in the Intelligent Environment

et al. 2021

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In the context of hyper-connected cars and a growing heterogeneous digital ecosystem, we wish to make the most of the data available from the various sensors, devices and services that compose the ecosystem, in order to propose a proof of concept in-vehicle system that enhances the driving experience. We focus on improving the driving experience along three main directions, namely: (1) driving and trip planning, (2) health and well-being and (3) social and online activities. We approached the in-vehicle space as a smart interface to the intelligent driving environment. The digital data-producers in the ecosystem of the connected car are sources of raw data of various categories, such as data from the outside world, gathered from sensors or online services, data from the car itself and data from the driver gathered with various mobile and wearable devices, by means of observing his state and by means of his social media and online activity. Data is later processed into three information categories—driving, wellness, and social—and used to provide multi-modal interaction, namely visual, audio and gesture. The system is implemented to act in response to the trafficked information on different levels of autonomy, either in a reactive manner, by simple monitoring, or in a proactive manner. The system is designed to provide an in-vehicle system that assists the driver with planning the travel (Drive panel), by providing a comfortable environment for the driver while monitoring him (Wellness panel), and by adaptively managing interactions with their phone and the digital environment (Social panel). Heuristic evaluation of the system is performed, with respect to guidelines formulated for automated vehicles, and a SWOT analysis of the system is also presented in the paper.

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Demonstration of Mobile Edge Cloud for 5G Connected Cars

Cited by 12 publications

References 1 publication

Blockchain-based Route Selection with Allocation of Radio and Computing Resources for Connected Vehicles

Blockchain-based Route Selection with Allocation of Radio and Computing Resources for Connected Vehicles

An edge abstraction layer enabling federated and hierarchical orchestration of CCAM services in 5G and beyond networks

Ubiquitous Computing: Driving in the Intelligent Environment

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