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eISSN: 2581-9615 || CODEN (USA): WJARAI || Impact Factor: 8.2 || ISSN Approved Journal

Study on IoT-based vehicle accident-avoidance system

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S. V. Vigneshvar * and R. Vadivel

Department of Information Technology, Bharathiar University, Coimbatore, Tamil Nadu, India– 641046.

Review Article

World Journal of Advanced Research and Reviews, 2025, 26(01), 1596-1603

Article DOI: 10.30574/wjarr.2025.26.1.0834

DOI url: https://doi.org/10.30574/wjarr.2025.26.1.0834

Received on 08 February 2025; revised on 16 March 2025; accepted on 19 March 2025

Presents an efficient and secure platooning strategy for Industry 4.0 environments involving Automated Guided Vehicles. The strategy proposed adopts Threat and Operability (THROP) and Hazard and Operability (Hazard and Operability) to determine and eliminate hazards like system failures and cyberattacks. Adaptive risk management and real-time monitoring are guaranteed using digital twin-based simulations, with enhanced AGV coordination and collision risk reduced. The system also provides encryption and authentication to provide integrity to data. Simulation shows improved scalability, security, and efficiency, and potential use in smart cities and logistics. Large-scale deployment and AI-based predictive analytics are areas of interest for future study. This study helps advance industrial automation in Industry 4.0 through ensuring safe and reliable AGV operations.

Convolutional Neural Networks (Cnns); Deep Learning; Computer Vision; Image Processing

https://journalwjarr.com/sites/default/files/fulltext_pdf/WJARR-2025-0834.pdf

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S. V. Vigneshvar and R. Vadivel. Study on IoT-based vehicle accident-avoidance system. World Journal of Advanced Research and Reviews, 2025, 26(01), 1596-1603. Article DOI: https://doi.org/10.30574/wjarr.2025.26.1.0834.

Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0

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