Predicting traffic density and increasing fuel efficiency in vehicles using secure vehicular networks

Srishti Bhargava, Krishna Prakasha, Ishita Sinha

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The number of on-road vehicles has increased by almost 140% over the past decade, thereby increasing pollution levels, congestion and fuel consumption. To contain such a situation, Vehicular Ad-Hoc Network (VANET) technology is almost on the verge of large scale practical deployment. Hence, the aim is to provide a system to control the above using VANET and to provide an infrastructure to reduce the commute time, traffic congestion and increase fuel efficiency as well as secure communication between vehicles. An algorithm is proposed, which uses the concept of weighted variables and Dijkstra's to predict the most viable path with the least traffic density and congestion. However, this system is prone to various active and passive attacks, therefore, to deal with the issue of secure transfer of information between the OBU's and the cloud, a security mechanism is established, central to which would be the Certification Authority (CA), responsible for registering vehicles, monitoring loyalty and distribution of keys. This security infrastructure uses pre-existing algorithms like RSA and Hashing to obtain viable results. Thus, a secure system can be established which would route the drivers to the best possible path and notify them in case of an imminent traffic jam thereby increasing fuel efficiency. The feasibility and performance of the algorithm was tested via simulation due to the lack of real time data. The simulation was performed on open source platforms such as SUMO (Simulation of Urban Mobility). The proposed algorithm gave much better results than most of the existing counterparts or routing without any interference.

Original languageEnglish
Title of host publication2017 International Conference on Computer Communication and Informatics, ICCCI 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467388542
DOIs
Publication statusPublished - 21-11-2017
Event7th International Conference on Computer Communication and Informatics, ICCCI 2017 - Coimbatore, India
Duration: 05-01-201707-01-2017

Conference

Conference7th International Conference on Computer Communication and Informatics, ICCCI 2017
CountryIndia
CityCoimbatore
Period05-01-1707-01-17

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Signal Processing

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    Bhargava, S., Prakasha, K., & Sinha, I. (2017). Predicting traffic density and increasing fuel efficiency in vehicles using secure vehicular networks. In 2017 International Conference on Computer Communication and Informatics, ICCCI 2017 [8117709] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCCI.2017.8117709