Cauchy Density-based Algorithm for VANETs Clustering in 3D Road Environments

• Main Authors: AL-Obaidi, A.S., Jubair, M.A., Aziz, I.A., Ahmad, M.R., Mostafa, S.A., Mahdin, H., AL-Tickriti, A.T., Hassan, M.H.
• Format: Article
• Published: Institute of Electrical and Electronics Engineers Inc. 2022
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133747632&doi=10.1109%2fACCESS.2022.3187698&partnerID=40&md5=1ee05521d118a67917f066233ad0bf9d; http://eprints.utp.edu.my/33385/

Vehicular ad hoc networks (VANETs) are emerging to serve various types of applications for serving smart cities and intelligent transportation systems. There are several challenging factors for ensuring reliable and stable VANETs communications. VANETs clustering is essential functionality to serve routing protocols and in enable reliable VANETs. Clustering algorithms for VANETs operate in decentralized mode, which requires incorporating additional stages before deciding the clustering decisions and might create suboptimality due to the local nature of the decentralized approach. In addition, the challenging architecture of the road environment can cause confusing clustering decisions. This problem becomes more challenging due to the evolving nature of clusters in VANETs in general and in 3D VANETs in particular. This paper tackles the problem of 3D VANETs using a centralized clustering model based on the developed Cauchy density model. The development has included a traffic generation model, mobility model for generating driving behavior, and an algorithm for enabling modeling of road curvature based on an adjacency list that defines the road’s points that define a straight-line segment. In addition, a clustering algorithm that defines mobility vector and uses Cauchy-based density for enabling adding vehicles to their respective clusters. For evaluation, conducted scenarios in three locations with 3D architecture are considered, and a comparison with benchmarks shows the superiority of our developed over the benchmarks with an improvement percentage of 1, 10, and 3 for average cluster head duration, average cluster member duration, and clustering efficiency respectively. Author