Skip to content
akaturk Academic measurement
For academics Sign in Sign up

Article detail · 2016

CACONET: Ant Colony Optimization (ACO) Based Clustering Algorithm for VANET

Journal

PLOS ONE

ISSN 1932-6203

YÖKSİS OpenAlex Open access · gold SJR Q1 JCR Q1 Citations 132 Top 10% Percentile 95.4% FWCI 4.86
Year
2016
Type
article

Data source split

  • YÖKSİS YÖKSİS article record
  • YÖKSİS venue PLOS ONE
  • Catalog match (ISSN) PLOS ONE
  • OpenAlex OpenAlex enrichment (abstract, citations, topics)

Abstract

OpenAlex · English

A vehicular ad hoc network (VANET) is a wirelessly connected network of vehicular nodes. A number of techniques, such as message ferrying, data aggregation, and vehicular node clustering aim to improve communication efficiency in VANETs. Cluster heads (CHs), selected in the process of clustering, manage inter-cluster and intra-cluster communication. The lifetime of clusters and number of CHs determines the efficiency of network. In this paper a Clustering algorithm based on Ant Colony Optimization (ACO) for VANETs (CACONET) is proposed. CACONET forms optimized clusters for robust communication. CACONET is compared empirically with state-of-the-art baseline techniques like Multi-Objective Particle Swarm Optimization (MOPSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO). Experiments varying the grid size of the network, the transmission range of nodes, and number of nodes in the network were performed to evaluate the comparative effectiveness of these algorithms. For optimized clustering, the parameters considered are the transmission range, direction and speed of the nodes. The results indicate that CACONET significantly outperforms MOPSO and CLPSO.

Topics

Citations

OpenAlex cited_by_count. Not a WoS or Scopus citation count; those sources have no separate column here.

132 citations

OpenAlex cited_by_count (cache / database)

Authors

  1. FARHAN AADIL SİVAS BİLİM VE TEKNOLOJİ ÜNİVERSİTESİ