A cost effective real time rail track monitoring system leveraging multi sensor fusion and multi objective optimization.

Derailments, common in developing nations, often result from dislodged or defective rail blocks, leading to substantial property damage and loss of life. Developing an automated real-time wireless sensing system for preventing derailments is a complex challenge, particularly in resource-constrained regions with limited network infrastructure. Existing research has yet to provide a practical solution that effectively addresses the need for long-distance sensing and optimized sensor deployment. This research focuses on developing a comprehensive solution that addresses these challenges. We explore vibration sensing and multi-sensor fusion for accurate rail track detection. Through rigorous experimentation and analysis, we demonstrate a 95% accuracy rate in detecting incoming trains from a distance of 1 km. To optimize sensor deployment, we contrive a multi-objective optimization problem and employ a meta-heuristic approach. We generate effective sensor deployment topologies by considering factors such as cost, vulnerability, and accident statistics. Our findings reveal that optimized deployment can significantly reduce the deployment costs up to 50% while preventing over 65% of potential accidents compared to a brute-force deployment approach. This research offers a promising solution for real-time rail track detection, combining advanced technologies and data-driven optimization. The findings contribute to enhancing railway safety and mitigating the devastating consequences of derailments.