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Proceedings ArticleDOI

Vehicular networks for reduction of fuel consumption and CO 2 emission

TLDR
The main goals of this paper are to motivate communications researchers to design EEFG protocols, demonstrate the ability to integrate fuel and emission models with vehicular networks, and illustrate the benefit of transmitting the traffic light signal information to vehicles for fuel consumption and emission reduction.
Abstract
With recent advances in the development of wireless communication networks, vehicular networks have been receiving considerable research interest. One of the major applications of vehicular networks is Intelligent Transportation Systems (ITS). To exchange and distribute messages, geocast routing protocols have been proposed for ITS applications. Almost all of these protocols evaluate network-centric performance measures, instead of evaluating the impact of the protocol on the vehicular system. Nowadays, rising fuel costs and the harmful effects of air pollutants have been the subject of considerable public debate. Therefore, it is desirable to create new economical and environmentally friendly geocast (EEFG) protocols, which focus on minimizing vehicle fuel consumption and emissions. The main goals of this paper are to motivate communications researchers to design EEFG protocols, demonstrate the ability to integrate fuel and emission models with vehicular networks, and illustrate the benefit of transmitting the traffic light signal information to vehicles for fuel consumption and emission reduction. By means of an example, we show how vehicular networks can be used to reduce fuel consumption and carbon dioxide (CO 2 ) emission in a city environment. Simulation results demonstrate that vehicle fuel consumption and CO 2 emission will be reduced if such an EEFG protocol is used.

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Journal ArticleDOI

Vehicular Networks for a Greener Environment: A Survey

TL;DR: This paper is meant to motivate researchers to investigate a new direction in which a network of vehicles is used to enhance total fuel and power consumption, gas emissions, and-as a result-budgets.
Journal ArticleDOI

Optimization of Fuel Cost and Emissions Using V2V Communications

TL;DR: This paper proposes a comprehensive optimization model that involves V2V and TLS2V communications to minimize fuel consumption by and emissions from vehicles and proposes efficient heuristic expressions to compute the optimum or near-optimum value of SR.
Journal ArticleDOI

An Open Traffic Light Control Model for Reducing Vehicles' $\hbox{CO}_{2}$ Emissions Based on ETC Vehicles

TL;DR: Simulation results indicate that the proposed scheme performs much better than the adaptive fuzzy traffic light control method: the average waiting time, short-time stop times, and CO2 emissions are greatly reduced, and the nonstop passing rate is greatly improved.
Proceedings ArticleDOI

VANET-Enabled Eco-Friendly Road Characteristics-Aware Routing for Vehicular Traffic

TL;DR: Simulation-based tests showed that by using EcoTrec, fuel emissions were significantly reduced, when compared with existing state-of-the-art vehicular routing algorithms.
References
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Journal ArticleDOI

Estimating vehicle fuel consumption and emissions based on instantaneous speed and acceleration levels

TL;DR: In this paper, the authors presented several hybrid regression models that predict hot stabilized vehicle fuel consumption and emission rates for light-duty vehicles and lightduty trucks, using data collected at the Oak Ridge National Laboratory (ORNL).
Journal ArticleDOI

Inter-vehicle communication systems: a survey

TL;DR: This article presents several major classes of applications and the types of services they require from an underlying network and analyzes existing networking protocols in a bottom-up fashion, from the physical to the transport layers, as well as security aspects related to IVC systems.
MonographDOI

Vehicular Networks: Techniques, Standards, and Applications

TL;DR: Vehicular Networks: Techniques, Standards and Applications examines the latest advances in the evolution of vehicular networks, presenting invaluable state-of-the-art ideas and solutions for professionals and academics at work on numerous international development and deployment projects.
Book

Intelligent Traffic Light Control

TL;DR: This paper studies the simulation and optimization of traffic light controllers in a city and presents an adaptive optimization algorithm based on reinforcement learning that outperform other fixed controllers on all studied infrastructures.
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