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Generating the Appropriate Route for the Detection of Data Congestion in VANETs using a Clustering Approach

Version 2 2024-06-04, 02:39
Version 1 2021-03-31, 16:51
conference contribution
posted on 2024-06-04, 02:39 authored by Tarandeep Kaur Bhatia, Ramkumar Ketti Ramachandran, Robin Ram Mohan DossRobin Ram Mohan Doss, Lei PanLei Pan
Vehicular Ad-hoc NETworks (VANETs) is a specific type of MANETs class as it deals with the safety of the vehicular nodes moving on the roads. As the VANETs field has a high movement rate, choosing the best routing protocol in this domain is a difficult task. Therefore, this paper reveals that the AODV (Ad-hoc On-Demand Distance Vector) routing protocol is regarded as one of the most suitable protocols for the VANETs field. During the route generation process, AODV transmits RREQ (Route-Request message) and generates several additional routes among an origin and a target node. This paper focuses on enhancing and improving the AODV performance by applying the novel clustering approach to generate constant clusters. In this paper, initially, the VANETs network’s deployment for the route discovery and later generation of the appropriate route using the AODV routing protocol with a clustering approach is mentioned. Finally, four parameters are calculated for each route. The performance of three chosen protocols and the proposed algorithm is analyzed based on the defined parameters such as Throughput, Packet Delivery Ratio (PDR), End-to-End Delay, Packet Loss Ratio (PLR). The outcomes revealed that our proposed algorithm performs better than the other algorithms. The proposed algorithm gives the highest value for the throughput and PDR and the minimum value for the delay and PLR.

History

Pagination

636-642

Location

Online

Start date

2021-01-28

End date

2021-01-29

ISBN-13

9781665414517

Language

eng

Publication classification

E1 Full written paper - refereed

Title of proceedings

Confluence 2021 : Proceedings of the 11th International Conference on Cloud Computing, Data Science & Engineering

Event

Cloud Computing, Data Science & Engineering. Conference ( 2021 : 11th : Online)

Publisher

IEEE

Place of publication

Piscataway, N.J.

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