Evaluation of LTE-Advanced connectivity options for the provisioning of V2X services

Vehicle-to-everything (V2X) communication and services have been garnering significant interest from different stakeholders as part of future intelligent transportation systems (ITSs). This is due to the many benefits they offer. However, many of these services have stringent performance requirements, particularly in terms of the delay/latency. Multi-access/mobile edge computing (MEC) has been proposed as a potential solution for such services by bringing them closer to vehicles. Yet, this introduces a new set of challenges such as where to place these V2X services, especially given the limit computation resources available at edge nodes. To that end, this work formulates the problem of optimal V2X service placement (OVSP) in a hybrid core/edge environment as a binary integer linear programming problem. To the best of our knowledge, no previous work considered the V2X service placement problem while taking into consideration the computational resource availability at the nodes. Moreover, a low-complexity greedy-based heuristic algorithm named "Greedy V2X Service Placement Algorithm" (G-VSPA) was developed to solve this problem. Simulation results show that the OVSP model successfully guarantees and maintains the QoS requirements of all the different V2X services. Additionally, it is observed that the proposed G-VSPA algorithm achieves close to optimal performance while having lower complexity. 1) Communication Modes: To cover all possible on-road interactions, the 3GPP project proposed four different communication modes. This includes vehicle-to-network (V2N), vehicle-to-infrastructure (V2I), vehicle-to-vehicle (V2V), and vehicle-to-pedestrian (V2P) communication [5] as shown in Fig. 2. Depending on the service or application, a communication mode can be chosen. A brief overview of each of these communication modes is provided below: a-V2N Communication: V2N communication refers to the communication between a vehicle and a V2X application server. This is typically done using a cellular network such as an LTE network [9], [10]. Through this connection, different services such as infotainment, traffic optimization, navigation, and safety can be offered [11], [12].

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“…Note that the LTE-A supports V2X communication through the Uu-based and the PC5-based interfaces [37]- [40].…”

Section: A System Setupmentioning

confidence: 99%

Edge-Enabled V2X Service Placement for Intelligent Transportation Systems

Moubayed

Shami

Heidari

et al. 2021

IEEE Trans. on Mobile Comput.

143

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“…According to the standard [2], there are three selection window sizes, with their respective reselection counter selection ranges. To this end, the standard includes Γ = 100 ms with RC ∈ [5,15], Γ = 50 ms with RC ∈ [10,30] and Γ = 20 ms with RC ∈ [25,75]. In this paper, we consider Γ = 100 ms with RC ∈ [5,15], and the remaining two scenarios will be considered in our future work.…”

Section: A Semi-persistant Scheduling Algorithmmentioning

confidence: 99%

“…We consider a highway with four parallel lanes in each direction. We assume that the vehicles move at a constant speed of 100 km/h on the highway, and the average intervehicle distance is 50 m. We assume that only CAM and DENM are utilized for V2V communication [9], [10], where their reference packet formats are specified by the European telecommunications standards institute (ETSI) [8]. CAM, which are keep alive messages, are periodically broadcast by each vehicle to provide information of presence, position, temperature, and primary status.…”

Section: A Instantiation Of Cam and Denm In Highwaymentioning

confidence: 99%

A Markov Perspective on C-V2X Mode 4

N.B.A.

Haapola

Samarasinghe

2019

2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)

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This paper proposes a multi-dimensional Markov model to evaluate the medium access control (MAC) layer performance of 3GPP cellular vehicle to everything (V2X) Mode 4. The Mode 4 specifications enable quality of service guarantees in a decentralized manner, without the connectivity of a cellular base station, and therefore, ideal for V2X messaging in intelligent transportation systems (ITS). To this end, periodic cooperative aware messages (CAMs) and event triggered decentralized environmental notification messages (DENMs) are considered in the paper. The discrete time Markov model consists of a node model, a queue model, and traffic generators for CAM and DENM packets that are dependent and solved in closed form and iteratively. The model is applied to a highway scenario to provide insights on the average delay, the collision probability, and the channel utilization in Mode 4. The results show that Mode 4 has been partially over-specified for ITS applications and able to serve CAMs and DENMs conveniently. However, there exists locally optimal combinations of CAM and DENM intervals that can lead to lower average delay.

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“…On this basis, new tools have emerged, which integrate urban traffic control systems to a simulated environment; one of the most popular of them are the V2X technologies (vehicle to X), where X means any object or system. This technology refers to the implementation of Transportation Cyber-Physical System (TCPS) [6,7]. The objective of V2X technologies is that each vehicle within an urban traffic system communicates through a network with other vehicles, elements, or objects within the same system, as well as the urban infrastructure remaining updated about the current traffic situation [8].…”

Section: Introductionmentioning

confidence: 99%

A Cyber-Physical System Modelling Framework for an Intelligent Urban Traffic System

Mancilla¹,

Neri²,

Dominguez³

2019

CyS

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This paper presents a novel modelling framework for a component-based architecture that allows validating control strategies for urban traffic networks integrating intelligent traffic control based on cyber-physical transport systems, specifically using V2X (vehicle-to-vehicle) technology. The architecture named ITSiE (Integrated tools in a Simulated Environment), is constituted of three components that work in a cooperative way via a middleware, allowing to analyze the traffic behavior in a simulated environment for a defined urban area (selected network streets) and evaluate the impact of using intelligent transport strategies based in V2X. The first component named CiudadelaSIM executes the simulation and communicate in real time with the other components; the second component named SCPV (Road Proposal Capture System (by its meaning in Spanish), is used to define the simulated scenario (traffic lights, traffic policies, information objects) and the third component is a Control Speed System (SpeCo) which allow defining the intelligent strategy, through the capture of multiple techniques and methods to control the speed of the vehicles in a segment with particular features.

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Evaluation of LTE-Advanced connectivity options for the provisioning of V2X services

Cited by 33 publications

References 9 publications

Edge-Enabled V2X Service Placement for Intelligent Transportation Systems

Edge-Enabled V2X Service Placement for Intelligent Transportation Systems

A Markov Perspective on C-V2X Mode 4

A Cyber-Physical System Modelling Framework for an Intelligent Urban Traffic System

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