• SAILAKSHMI CHITTAMURU
• D.PRASAD REDDY

The development of adaptive learning mechanisms within ARF-VTAD, such as online learning and continual learning techniques, could allow the system to refine its performance over time by learning from newly observed driving patterns and traffic behaviours. This would enhance its robustness against changing road conditions, weather variations, and other external factors that may influence vehicle performance. Lastly, the security framework of ARF-VTAD could be strengthened by integrating blockchain-based data integrity mechanisms. Blockchain technology can ensure secure data transmission between vehicles and central servers, protecting sensitive information from cyber threats and unauthorized access. This enhancement would be crucial for applications involving autonomous vehicles and large-scale transport networks, where data security is a critical concern. In conclusion, the ARF-VTAD system holds the potential to transform modern transportation networks through its adaptability, scalability, and proactive monitoring capabilities. As future developments unfold, this system is expected to play a key role in the creation of safer, more efficient, and sustainable transportation systems globally

GPS, Enhanced ShockBurst, vehicle tracking,NRF24101+PA LNA

"NOVAL APPROCH TO REAL-TIME VEHICLE TARCKING SYTEM USING RANDOM FOREST ALGORITHM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.a23-a30, March-2025, Available :http://www.jetir.org/papers/JETIR2503004.pdf

"NOVAL APPROCH TO REAL-TIME VEHICLE TARCKING SYTEM USING RANDOM FOREST ALGORITHM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppa23-a30, March-2025, Available at : http://www.jetir.org/papers/JETIR2503004.pdf