Optimized clustering in vehicular ad hoc networks based on honey bee and genetic algorithm for internet of things

Masood Ahmad, Ataul Aziz Ikram, Ishtiaq Wahid, Fasee Ullah*, Awais Ahmad, Fakhri Alam Khan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

In vehicular ad hoc network (VANET), the size of routing table can be reduced with the help of clustering architecture. The frequent changes in topology are the noteworthy characteristics of a VANET as its nature is dynamic. To manage the topology dynamics in VANET with less overhead, the concept of clustering can be used. Henceforth, an effective procedure that adjusts quickly to the topology changes should be designed. Firstly, the clustering problem (CP) in VANET is formulated into a dynamic optimization problem in this paper. Secondly, an optimization algorithm named Vehicular Genetic Bee Clustering (VGBC) based on honey bee algorithm and properties of genetic algorithm solves the CP in VANETs is suggested. In VGBC, individuals (bees) represent a realistic clustering structure and its fitness is measured on the basis of load balancing and stability. A technique that merges the properties of genetic algorithm and honey bee algorithm is proposed. It helps the population to handle the topology changes and harvest high quality solutions. The simulation results piloted for justification demonstrate that the VGBC form steady and balanced clusters. The simulation results are matched with state of the art clustering schemes in VANET. The VGBC outperform existing schemes in terms of cluster count, cluster duration, re-affiliation rate, computational overhead, load balancing, VANET lifetime and clustering overhead.

Original languageEnglish
Pages (from-to)532-547
Number of pages16
JournalPeer-to-Peer Networking and Applications
Volume13
Issue number2
DOIs
StatePublished - 1 Mar 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2019, Springer Science+Business Media, LLC, part of Springer Nature.

Keywords

  • Cluster
  • Genetic algorithm
  • Honey bee algorithm
  • Optimization
  • VANETs

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications

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