Vehicles–to-Infrastructure Communication Safety Messaging in DSRC

Nwizege, Kenneth S.; Bottero, Mauro; Shedrack, Mmeah; Nwiwure, Emmanuel D.

doi:10.1016/j.procs.2014.07.070

Cited by 13 publications

(6 citation statements)

References 3 publications

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“…The speed of the vehicles was modeled using (2)(3)(4)(5)(6) to constrain the vehicles within the speed limit. From the graph obtained in Fig.7, result shows that the vehicles clustered within the speed limit for the simulation.…”

Section: A Evaluation Of Resultsmentioning

confidence: 99%

“…From the results, network signal degrade as the MNs move further from the base station. It also deteriorates when congestion increases on the network as seen in (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16) showing distance relationships. Any of the MNs depending on feedback from the base station would degrade in signal reception because of congestion, and this occurs as earlier seen when the number of nodes increases.…”

Section: A Evaluation Of Resultsmentioning

confidence: 99%

“…In the ConstSpeed mobility model, MNs can move along a lane with constant speed to a randomly chosen target, and when the target is reached, it will randomly select a new one. With this model, MNs select their speeds uniformly over this range as shown in (1)(2)(3)(4)(5)(6). Initial positions of MNs are generated randomly.…”

Section: B Constant Speed (Constspeed) Mobility Modelmentioning

confidence: 99%

“…In this paper, derived mobility metrics is used to implement the impact of mobility in mobile networks with an Adapted Context-http://dx.doi.org/10.12785/ijcds/070302 http://journals.uob.edu.bh Aware Rate Selection (ACARS) mobility model. ACARS is one of the RAs used in this simulation to implement mobility model [6].…”

Section: Introductionmentioning

confidence: 99%

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Using Derived Mobility Model Metrics to Evaluate the Impact of Transmission Range in Vehicular Networks

Nwizege¹,

Ikhazuangbe²,

Philip-Kpae³

et al. 2018

IJCDS

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Mobile Ad-Hoc Networks (MANETs) is a spontaneous network that facilitates communication among nodes without a centralized-control device unlike the infrastructure network category. One of the major challenges with MANETs and Vehicular Ad-Hoc Networks (VANETS) in mobile communication is the problem of connectivity between Mobile Nodes (MNs). This dilemma occurs as a result of high mobility of nodes moving farther from each other in MANETs or VANETS or moving away from the base station as in a cellular network or in a Mobile Infrastructure Ad Hoc Networks (MI-VANETs) as a result of propagation effects. Mobility of nodes is one of the major impacts of routing in every mobile and wireless network because of high dynamic nature of mobile environments. Several factors that influence the evaluation of the networks depend mainly on the implementation of the mobility model and the mobility model in turn depends on several factors of the environment. This paper evaluates the impact of communication and transmission range on network connectivity due to high mobility of nodes in a mobile and wireless environment. The results obtained through derived mobility metrics and Rate Adaption (RA) show that communication range has impact on connectivity in a highly mobile environment such as in vehicular communications. MN in this context is same as vehicles in this article.

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Section: A Evaluation Of Resultsmentioning

confidence: 99%

Section: A Evaluation Of Resultsmentioning

confidence: 99%

Section: B Constant Speed (Constspeed) Mobility Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Using Derived Mobility Model Metrics to Evaluate the Impact of Transmission Range in Vehicular Networks

Nwizege¹,

Ikhazuangbe²,

Philip-Kpae³

et al. 2018

IJCDS

View full text Add to dashboard Cite

show abstract

“…In urban traffic system, due to blurred vision or distracting attention, drivers could not acquire the accurate signal phase and timing (SPaT) and distance information, which will generally lead to inappropriate driving behaviors, thus cause extra fuel consumption. Fortunately, vehicle-to-everything (V2X) communications have been widely applied to the analysis and construction of urban traffic scenarios [16][17][18]. It is entirely possible to receive the SPaT and distance information from hundreds of meters away the traffic signals relying on the reliable communication technology, which is beneficial for drivers to make more accurate decisions.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Eco-Driving on Signalized Arterial Corridors during the Green Phase for the Connected Vehicles

Zhao

Xin

et al. 2020

Journal of Advanced Transportation

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Inappropriate driving behaviours can result in additional fuel consumption and emissions. Drivers can be informed of the accurate signal phase and timing (SPaT) and distance information of the current intersection and downstream intersections via vehicle-to-everything (V2X) communications. The real-time information has been utilized to assist drivers in taking reasonable manoeuvres and gain lots of benefits on fuel consumption and emissions in some existing studies. In order to cooperatively address the optimization problem on the signalized arterial corridors, this paper presents an eco-driving optimization model considering preceding SPaT and position information. This model can be applied to pass two successive traffic signals cooperatively during green phase. In this study, a multi-stage optimal approach is proposed to minimize the fuel consumption. Field experiments are carried out for comparative analysis between the connected vehicle with speed advisory and the uninformed vehicle without speed advisory. The results indicate that the fuel saving of the connected vehicle guided by the dynamic optimization algorithm shows significant improvement. In addition, the rolling optimization among three signalized intersections is conducted and the results show that a considerable improvement can be obtained compared with the one-by-one optimization.

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A Smart Algorithm for CO2 Emissions Reduction on Realistic Traffic Scenarios Exploiting VANET Architecture

Sottile

Cordeschi

Amendola

2020

Communications in Computer and Information Science

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Vehicles–to-Infrastructure Communication Safety Messaging in DSRC

Cited by 13 publications

References 3 publications

Using Derived Mobility Model Metrics to Evaluate the Impact of Transmission Range in Vehicular Networks

Using Derived Mobility Model Metrics to Evaluate the Impact of Transmission Range in Vehicular Networks

Dynamic Eco-Driving on Signalized Arterial Corridors during the Green Phase for the Connected Vehicles

A Smart Algorithm for CO2 Emissions Reduction on Realistic Traffic Scenarios Exploiting VANET Architecture

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