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Original scientific paper

https://doi.org/10.1080/00051144.2024.2307232

Prioritization and scheduling in the vehicle-to-infrastructure (V2I) scheme for vanets using enhanced congestion control source based ant colony optimization

V. Lekshmi ; Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, Thackalay, India *
R. Suji Pramila ; Department of Computer Science and Engineering, Mar Baselios Institute of Technology and Science, Nellimattom, India

* Corresponding author.


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Abstract

Mobile ad hoc networks (MANETs), which include vehicular ad hoc networks (VANETs), are a
promising method for intelligent transportation systems. The major challenges of VANET protocols are symmetric links and changing topology of the networks and hence a proper routing
method is necessary for VANET. A crucial issue with VANETs is the transmission of data from a
starting point to the base station. To discover the least path with the lowest cost and fewest hops
between the source and the destination, existing work uses a novel priority-based directionaware collision avoidance (P-DVCA). To direct the ants to develop a shorter path with fewer
hops for data transmission, the suggested method uses the prioritising and scheduling enhanced
congestion control-based source ant colony optimization (ECC-ACO) for VANETs. Based on the
message type and network situation, this technique provides precedence for privacy messages
communications. Privacy messages can also be divided into beacon messages and occurrence
messages. At that point, each message is routed into the appropriate queue predicated on its primary consideration, and each message in each queue is planned. When compared to renowned
VANET communication architecture, simulation and analytical findings show that proposed
ECC-ACO offers a respectable reduction in collisions.

Keywords

Congestion control model; Source-based Ant colony optimization; VANET; Prioritization and scheduling

Hrčak ID:

326086

URI

https://hrcak.srce.hr/326086

Publication date:

26.2.2024.

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