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An Intelligent Traffic Signal Management Strategy to Reduce Vehicles CO2 Emissions in Fog Oriented VANET

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Abstract

The explosive increase in the number of vehicles causes an increase in traffic congestion and environmental pollution due to CO\(_{2}\) emission from vehicles. This increases the requirements of communication and computation resources as well. The advent of fog computing has served a vital role for providing easy access to communication and computation resources by moving these resources toward the edge of the network. In the present work three different Fog computing oriented VANET based approaches in Intelligent Transportation System for reducing traffic congestion and CO\(_{2}\) emission are proposed. All the three approaches provide recommended speed to the vehicles for reducing the number of stops, rate of acceleration and deceleration which in turn helps to reduce CO\(_{2}\) emission. The performances of all the three approaches are studied qualitatively and quantitatively. The qualitative analysis is evaluated in terms of communication, storage and computation overhead whereas the quantitative analysis is evaluated in terms of both software and hardware based simulation.

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Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

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  1. IIEST, Shibpur, Howrah, India

    Ananya Paul, Jaka Haricharan & Sulata Mitra

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  1. Ananya Paul
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  2. Jaka Haricharan
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  3. Sulata Mitra
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Correspondence to Ananya Paul.

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Paul, A., Haricharan, J. & Mitra, S. An Intelligent Traffic Signal Management Strategy to Reduce Vehicles CO2 Emissions in Fog Oriented VANET. Wireless Pers Commun 122, 543–576 (2022). https://doi.org/10.1007/s11277-021-08912-3

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