Proactive Railway Cable Infrastructure Protection Using Wireless Sensor Networks and Time Series Analytics

Abstract

In recent years, protecting railway cable infrastructure has become increasingly important due to a surge in theft, vandalism, and system failures, many of which have led to costly disruptions and safety concerns. This study introduces a practical, data-driven approach that combines wireless sensor networks (WSNs) with time-series analysis to improve how we monitor and respond to such threats. The proposed method unfolds in three stages: first, a detailed analysis of sensor data collected across several stations; second, the use of clustering techniques to identify unusual patterns; and third, the application of forecasting models (ARIMA and DCS Gaussian) to anticipate potential cable sagging or theft. We tested the system using both real-world sensor deployments and publicly available datasets. Results show high reliability, with prediction errors as low as RMSE = 0.0212 and MAPE = 0.23%, and clear correlations between cable behavior across stations. Overall, the framework offers a timely and cost-effective solution for improving railway infrastructure security and contributes useful insights to the growing field of AI-based transport monitoring.