An Adaptive Vehicle Clustering Algorithm Based on Power Minimization in Vehicular Ad-Hoc Networks

Haitao Zhao, Jiawen Tang, Bamidele Adebisi, Tomoaki Ohtsuki, Guan Gui, Hongbo Zhu

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we propose an adaptive vehicle clustering algorithm based on fuzzy C-means algorithm, which aims at minimizing power consumption of the vehicles. Specifically, the proposed algorithm firstly dynamically allocates the computing resources of each virtual machine in the vehicle, according to the popularity of different virtualized network functions. The optimal clustering number to minimize the total energy consumption of vehicles is determined using the fuzzy C-means algorithm and the clustering head is selected based on vehicles moving direction, weighted mobility, and entropy. Simulation results are provided to confirm that the proposed algorithm can decrease the power consumption of vehicles while satisfying the vehicle delay requirement.

Original languageEnglish
Pages (from-to)2939-2948
Number of pages10
JournalIEEE Transactions on Vehicular Technology
Volume71
Issue number3
DOIs
Publication statusPublished - 2022 Mar 1

Keywords

  • Internet of vehicle
  • edge computing
  • fuzzy C-means
  • power consumption
  • vehicle clustering

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics

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