Vol.33, No.1, 2022, pp.601-617, doi:10.32604/iasc.2022.023783
OPEN ACCESS
ARTICLE
VANET: Optimal Cluster Head Selection Using Opposition Based Learning
  • S. Aravindkumar*, P. Varalakshmi
Department of Computer Technology, Anna University–MIT Campus, Chennai, 600044, Tamilnadu, India
* Corresponding Author: S. Aravindkumar. Email:
Received 21 September 2021; Accepted 19 November 2021; Issue published 05 January 2022
Abstract

Traffic related accidents and route congestions remain to dwell significant issues in the globe. To overcome this, VANET was proposed to enhance the traffic management. However, there are several drawbacks in VANET such as collision of vehicles, data transmission in high probability of network fragmentation and data congestion. To overcome these issues, the Enhanced Pigeon Inspired Optimization (EPIO) and the Adaptive Neuro Fuzzy Inference System (ANFIS) based methods have been proposed. The Cluster Head (CH) has been selected optimally using the EPIO approach, and then the ANFIS has been used for updating and validating the CH and also for enhancing the data transmission procedures. The dijkstra’s algorithm has been used for identifying the shortest path for data transmission. The results showcases that the proposed technique has attained the maximum Packet Delivery Ratios (PDRs) as 73.23% at a sensor radius of 130 m and 70.42% at a velocity of 10 km/h. Moreover, the proposed method has outperformed the existing technique in terms of the CH formation delay, the end to end delay and the PDR.

Keywords
Vehicular NETworks (VANET); Enhanced Pigeon Inspired Optimization (EPIO); Adaptive Neuro Fuzzy Inference System (ANFIS); Cluster Head (CH)
Cite This Article
S. Aravindkumar and P. Varalakshmi, "Vanet: optimal cluster head selection using opposition based learning," Intelligent Automation & Soft Computing, vol. 33, no.1, pp. 601–617, 2022.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.