Real Time Flight Tracking Using Deep Learning and Blockchain Technology
Dr. Selvi S Department of Computer Science RMK Engineering College Chennai, India ssi.cse@rmkec.ac.in
Dr. T. Sethukarasi Department of Computer Science RMK Engineering College Chennai, India tsk.cse@rmkec.ac.in
Abstract--This paper presents the design and implementation of a 3D Real-Time Flight Tracking and continues Updates System, incorporating deep learning and block-chain technology, aimed at providing live updates on flight status, delays, and cancellations. The system also sends automated notifications via SMS or email for flight status changes. By leveraging advanced technologies, the system ensures data integrity, security and scalability, making it suitable for the aviation industry’s future needs. The aviation industry relies heavily on accurate and timely flight tracking for safety and operational efficiency. Traditional Systems, while effective, often lack predictive capabilities and robust data security measures. This paper proposes a novel approach that integrates deep learning algorithms for secure and immutable data logging. By leveraging Automatic Dependent Surveillance-Broadcast (ADS-B) data, Our system enhances real-time flight tracking with higher accuracy and reliability. We present a comprehensive system development and testing framework, demonstrating the effectiveness of our approach in a simulated environment. solution for real-time flight tracking and updates.
The innovative aspects of this system include the use of deep learning algorithms for accurate prediction of flight delays and cancellations based on real-time data from multiple sources, such as weather conditions, air traffic, and historical flight data. Block-chain technology is utilized to create a decentralized and tamper- proof ledger for recording flight data, ensuring the integrity and security of information shared among stakeholders, including airlines, airports. And passengers.
Keywords ~ Real-time flight tracking, deep learning, block-chain, automated notifications, aviation technology, predictive analytics.
