Department of Information Technology, Bharathiar University, Coimbatore, Tamil Nadu, India– 641046.
World Journal of Advanced Research and Reviews, 2025, 26(01), 1596-1603
Article DOI: 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
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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.
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