Citation

Wong, Wai Kit and Baskar, S. and Abubeker, K. M. and Ng, Poh Kiat (2025) Sustainable cyber-physical VANETs with AI-driven anomaly detection and energy-efficient multi-criteria routing using machine learning algorithms. Scientific Reports, 15 (1). ISSN 2045-2322

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Abstract

Cyber-physical systems have improved modern transportation by allowing vehicles and road systems to communicate through Vehicular Ad Hoc Networks (VANETs). Existing anomaly detection approaches often struggle with high false-positive rates, poor adaptability, and significant computational demands, compromising their real-time efficacy and scalability. To address these problems, this research presents an Anomaly Detection using Machine Learning Algorithms (AD-MLA) framework that employs a Random Forest model to accurately detect abnormal activities. The framework encompasses feature selection, data clustering, and an energy-efficient routing strategy that incorporates node energy, signal strength, hop count, and link stability. Evaluations demonstrate that AD-MLA reduces false alarms, improves detection accuracy, and operates with lower energy and computational requirements. It offers a smart, rapid, and efficient security system for real-time VANET environments, rendering it appropriate for transportation systems characterised by high reliability and safety. By integrating a Random-Forest-based anomaly detector with intelligent feature selection and an energy-efficient routing method that accounts for residual energy, signal strength, and link stability, the suggested framework systematically addresses these challenges. This approach delivers 95.33% accuracy, 96.09% recall, 94.25% computational efficiency, and 91.45% resource-use efficiency. This effectively addresses the scalability, latency, and energy challenges that previous systems have faced in incorporating blockchain technology and deep learning architectures.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	08 Dec 2025 00:50
Last Modified:	08 Dec 2025 00:50
URII:	http://shdl.mmu.edu.my/id/eprint/14969

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