Dynamic Clustering in IoV Using Behavioral Parameters and Contention Window Adaptation

Bimal Ghimire, Danda Rawat

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Internet of Vehicles (IoV) plays an indispensable role in intelligent transportation system (ITS). When the vehicle density is high, the performance of IoV suffers due to the broadcast storm problem. To address this issue, we propose a novel clustering approach using behavioral parameters and a current journey parameter. In this approach, we first calculate a cluster fitness score (CFS) for each vehicle in the network and select a vehicle with the highest CFS as a cluster head (CH). The CH then arranges two forms of communications which are contention free communication (CFC) and contention-based communication (CBC). In CFC, each cluster member (CM) broadcasts periodically without contending the channel access. However, in CBC, communicating parties like roadside units (RSUs), cluster heads (CHs), cluster members (CMs), and other vehicles contend to get the channel access. The contention window of CMs is also adapted to improve IoV performance. Additionally, our approach facilitates partitioning into sub-clusters and merging of sub-clusters based on the travel direction. Finally, we present the simulation results to demonstrate the improved performance of IoV.

Original languageEnglish (US)
Pages (from-to)2031-2040
Number of pages10
JournalIEEE Transactions on Vehicular Technology
Volume71
Issue number2
DOIs
StatePublished - Feb 1 2022

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Computer Networks and Communications
  • Automotive Engineering

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