MEC-Assisted End-to-End Latency Evaluations for C-V2X Communications

Emara, Mustafa; Filippou, Miltiades C.; Sabella, Dario

doi:10.1109/eucnc.2018.8442825

Cited by 77 publications

(53 citation statements)

References 9 publications

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“…We let ~(8,12) be the context size for task (hence, the size of the -th packet to be transmitted), measured in kbits. Moreover, we model the processing time at the MEC Host as = ( )⁄ , where ~(100,300) are the cycles per bit necessary for processing task and = 9 Gcycles/s is the processing capacity at the MEC [34].…”

Section: A Application Offloading With Mecmentioning

confidence: 99%

Simu5G–An OMNeT++ Library for End-to-End Performance Evaluation of 5G Networks

et al. 2020

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In this paper we introduce Simu5G, a new OMNeT++-based model library to simulate 5G networks. Simu5G allows users to simulate the data plane of 5G New Radio deployments, in an end-to-end perspective and including all protocol layers, making it a valuable tool for researchers and practitioners interested in the performance evaluation of 5G networks and services. We discuss the modelling of the protocol layers, network entities and functions, and validate our abstraction of the physical layer using 3GPP-based scenarios. Moreover, we show how Simu5G can be used to evaluate Multi-access Edge Computing (MEC) and Cellular Vehicle-to-everything (C-V2X) services offered through a 5G network.

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Section: A Application Offloading With Mecmentioning

confidence: 99%

Simu5G–An OMNeT++ Library for End-to-End Performance Evaluation of 5G Networks

et al. 2020

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“…In [29], they used VRUs (Vulnerable Road users) to assist in information collecting about road environments, and tell the vehicles about this information and then send it using cellular network. As a result, depending on MEC (Multi-access Edge Computing) [59] bases instead of cloud based cellular infrastructure, they can lower the endto-end latency significantly compared to conventional methods.…”

Section: ) Mec-assisted End-to-end Latency Evaluations For C-v2x Commentioning

confidence: 99%

Minimizing E2e Delay in V2x Over Cellular Networks: Review and Challenges

Ali

Al-Hemairy

2020

Iraqi Journal of ICT

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V2X (Vehicle-to-Everything) evolution over cellular networks has been an excelling topic with the advances of high throughput and low latency LTE networks, and the introduction of 5G networks. According to recent researches, obtaining acceptable End-to-End (E2E) delay has been a challenging design process since data travels through several steps from the originating source to the data center and vice versa. V2X latency comprises mainly of three levels: source processing, cellular network, and data center processing. Delay reduction can be achieved on the three levels. However, many conventional solutions have not reached the required and acceptable range of latency to enable V2X communication over cellular networks. In this paper, a general review of challenges to make V2X feasible on cellular network has been discussed, and the proposed solutions in the literature has been introduced. As a conclusion, a various types of aiding tools to design and test V2X tools are given, so that a right path should be taken to consider challenges and improving design metrics.

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“…The novelty here comes from the distribution and kind of road-side units used, developed as drones, but the MEC layer is directly mapped on physical devices without presenting a real advance. The improvement implied by MEC for vehicle to vulnerable road user communications was evaluated in [17], using 4G simulation and observing up to 80% of latency reduction as compared with a cloud-based system. In [18], the MEC paradigm was used to improve the performance of a delay-tolerant network, by providing caching of messages at the access network for temporally disconnected vehicles.…”

Section: Related Workmentioning

confidence: 99%

SURROGATES: Virtual OBUs to Foster 5G Vehicular Services

et al. 2019

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Virtualization technologies are key enablers of softwarized 5G networks, and their usage in the vehicular domain can provide flexibility and reliability in real deployments, where mobility and processing needs may be an issue. Next-generation vehicular services, such as the ones in the area of urban mobility and, in general, those interconnecting on-board sensors, require continuous data gathering and processing, but current architectures are stratified in two-tier solutions in which data is collected by on-board units (OBU) and sent to cloud servers. In this line, intermediate cache and processing layers are needed in order to cover quasi-ubiquitous data-gathering needs of vehicles in scenarios of smart cities/roads considering vehicles as moving sensors. The SURROGATES solution presented in this paper proposes to virtualize vehicle OBUs and create a novel Multi-Access Edge Computing (MEC) layer with the aim of offloading processing from the vehicle and serving data-access requests. This deals with potential disconnection periods of vehicles, saves radio resources when accessing the physical OBU and improves data processing performance. A proof of concept has been implemented using OpenStack and Open Source MANO to virtualize resources and gather data from in-vehicle sensors, and a final traffic monitoring service has been implemented to validate the proposal. Performance results reveal a speedup of more than 50% in the data request resolution, with consequently great savings of network resources in the wireless segment. Thus, this work opens a novel path regarding the virtualization of end-devices in the Intelligent Transportation Systems (ITS) ecosystem.

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MEC-Assisted End-to-End Latency Evaluations for C-V2X Communications

Cited by 77 publications

References 9 publications

Simu5G–An OMNeT++ Library for End-to-End Performance Evaluation of 5G Networks

Simu5G–An OMNeT++ Library for End-to-End Performance Evaluation of 5G Networks

Minimizing E2e Delay in V2x Over Cellular Networks: Review and Challenges

SURROGATES: Virtual OBUs to Foster 5G Vehicular Services

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