SSOCANET  SSOCANET - Empowering VANETs with Salp Swarm Optimization-Enhanced Clustering Algorithm

Zeeshan Hidayat; Zulfiqar Ali; Shahab Haider; Iftikhar Alam; Asad Ali

Authors

Zeeshan Hidayat Department of Computer Science, City, University of Science and Technology; Peshawar.
Zulfiqar Ali Department of Computer Science, City, University of Science and Technology; Peshawar.
Shahab Haider Department of Computer Science, City, University of Science and Technology; Peshawar.
Iftikhar Alam Department of Computer Science, City, University of Science and Technology; Peshawar.
Asad Ali Department of Computer Science, University of Engineering and Technology, Peshawar

Keywords:

Salp Swarm Optimization Algorithm, Vehicular Clustering., Bio-Inspired Clustering, VANETs, Vehicular Clustering

Abstract

Vehicular Ad hoc networks (VANETs) present significant challenges due to the dynamic nature of vehicle movements, leading to a constantly changing vehicular network topology. This instability results in packet loss, network fragmentation, message reliability, and scalability issues. To address these challenges, clustering has emerged as a promising solution to escalate vehicle communication efficiency. However, determining the optimal number of clusters remains a crucial problem. The proposed solution, the Salp Swarm Optimization-Enhanced Clustering Algorithm for VANET (SSOCANET), leverages the foraging behavior of salps to optimize cluster formation based on multiple objectives. SSOCANET achieves an optimal number of clusters by employing carefully designed objective functions, minimizing communication overhead and end-to-end communication latency in a network. The simulation results demonstrate the superior performance of SSOCANET compared to other clustering approaches, offering a robust solution for VANETs.

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