Data dissemination for green-VANETs communication: an opportunistic optimization approach. Issue 1 (1st December 2020)

• Record Type: Journal Article
• Authors: D., Chandramohan; Dumka, Ankur; V., Dhilipkumar; Loganathan, Jayakumar
• Is Part Of: International journal of pervasive computing and communications. Volume 17:Issue 1(2021)
• Page Start: 89
• Page End: 108
• Publication Date: 2020-12-01
• Subjects: Optimization -- Clustering -- Collision -- GVANET -- ITS -- IWD; Ubiquitous computing -- Periodicals; Mobile computing -- Periodicals; Computer network protocols -- Periodicals; Computer network architectures -- Periodicals; Application software -- Development -- Periodicals; 004.6
• DOI: 10.1108/IJPCC-04-2020-0030
• Languages: English
• ISSNs: 1742-7371

Abstract

Abstract : Purpose: This paper aims to predict the traffic and helps to find a solution. Unpredictable traffic leads more vehicles on the road. The result of which is one of the factors that aggravate traffic congestion. Traffic congestion occurs when the available transport resources are less when compared to the number of vehicles that share the resource. As the number of vehicles increases the resources become scarce and congestion is more. Design/methodology/approach: The population of the urban areas keeps increasing as the people move toward the cities in search of jobs and a better lifestyle. This leads to an increase in the number of vehicles on the road. However, the transport network, which is accessible to the citizens is less when compared to their demand. Findings: The demand for resources is higher than the actual capacity of the roads and the streets. There are some circumstances, which will aggravate traffic congestion. The circumstances can be the road condition (pot holes and road repair), accidents and some natural calamities. Originality/value: There is a lot of research being done to predict the traffic and model it to find a solution, which will make the condition better. However, still, it is an open issue. The accuracy of the predictions done is less.