Authors:
R. Mohan Das, M. Arunadevi Thirumalraj, T. Rajesh, S. Gopikha, Sureshkumar Somayajula

Addresses:
Department of Electrical and Electronic Engineering, New Horizon College of Engineering, Bengaluru, Karnataka, India. Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India. Department of Computer Science and Business Management, Saranathan College of Engineering, Tiruchirappalli, Tamil Nadu, India. Department of Electrical and Electronic Engineering, Malla Reddy Engineering College, Hyderabad, Telangana, India. Department of Information Technology, St.Joseph's College of Engineering, Chennai, Tamil Nadu, India. Department of Computer Science and Technology, Sunlife Canada Financials, Toronto, Ontario, Canada.

Abstract:

Vehicle-to-vehicle (V2V) communication enables a network of automobiles to perform collaborative computing, giving rise to the concept of a "vehicular cloud" (VC). However, without the need for edge nodes or cloud servers, vehicles can run applications that require massive processing cooperatively on their own by forming a Vehicular Ad-Hoc Network (VANET). Managing the recurrent topology alteration caused by vehicle mobility is a significant challenge for VANET cooperative computing. In this research, researchers present a V2V-based cooperative computing approach. The suggested method accounts for the distance between vehicles when selecting which vehicles to collaborate with, and it defers task offloading until the last possible moment to ensure a stable, energy-efficient cooperative computing environment. Despite its competitive performance compared to other MH algorithms, the artificial rabbits’ optimisation (ARO) algorithm still suffers from poor accuracy and the risk of local optima. Using antagonism methods, this research develops a selective opposition version of the artificial rabbit procedure (LARO) that eliminates the negative consequences of these shortcomings. To begin, during the random concealment phase, a Lévy flight strategy is implemented to increase population diversity and dynamics. The algorithm's convergence accuracy is enhanced by the richness of its various population samples. 

Keywords: Vehicular Cloud; Network Reliability; Lévy Flight; Artificial Rabbits Optimisation; Vehicular Ad-Hoc Network; Cooperative Computing Method; Vehicle-to-Vehicle.

Received on: 16/11/2024, Revised on: 24/01/2025, Accepted on: 01/03/2025, Published on: 05/09/2025

DOI: 10.69888/FTSIN.2025.000536

FMDB Transactions on Sustainable Intelligent Networks, 2025 Vol. 2 No. 3, Pages: 137-151

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