# Towards the Internet of Smart Trains:
## 6 IoT-Enabled Services: From More Efficient Operations to New Business Models
### Summary
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Service** & **Reference** & **Techniques** & **Main Contributions** \\ \hline [PERSON] et al. [64] & Expert systems & Anomaly detection in complex maintenance operations. Precision is in all cases above 90\% limiting both the number of false alarms and the number of undetected anomalies. \\ \hline [PERSON] et al. [65] & State-of-the-art, analytics, sensor fusion and Big & Precise location of a heavy freight train and its main parameters. Data \\ \hline [PERSON] et al. [66] & Sensors, optimization procedures & Adjust the maintenance needs and track speed limits dynamically using embedded sensors. Experimental results of the implementation. \\ \hline [PERSON] et al. [67] & State-of-the-art & Different strategies for preventive maintenance scheduling problem: hybrid genetic algorithms, ontology-based modeling, heuristic approaches and strategic gang scheduling. \\ \hline [PERSON] et al. [68] & Big Data & Maintenance decisions regarding railway tracks, all parts of the track can be monitored with appropriate intervals while maintaining the processing load within feasible limit. \\ \hline [PERSON] et al. [69; 70] & Expert systems, DSS, ontologies & Knowledge based systems to develop a prototype for maintenance scheduling. \\ \hline [PERSON] et al. [71; 72] & WSN, Zigbee & Monitoring system for slab track infrastructures using an energy consumption optimization strategy. \\ \hline [PERSON] et al. [73] & WSN, remote monitoring & Monitor the slope deformation, the variation in the internal stress and the PPV (Peak Particle Velocity) in an existing slope adjacent to a railway track. \\ \hline [PERSON] et al. [74] & WSN & Early warning system for infrastructure surveillance and threat detection. \\ \hline [PERSON] et al. [75] & Shapelet algorithms & Detecting replacement of Railway Point Machines (RPMs) using an electric current sensor. \\ \hline [PERSON] et al. [76] & State-of-the-art & Applications of modern predictive control methods, analysis tools and techniques for condition monitoring systems. \\ \hline [PERSON] et al. [77] & Ontologies, knowledge rules-based system & Tool to design complex infrastructures. \\ \hline [PERSON] et al. [78] & State-of-the-art & Trends in SHM \\ \hline [PERSON] et al. [79] & State-of-the-art & Overview about the static and dynamic behaviour of ballasted railway tracks in SHM. Estimation of stress transfer from the train passage to the track using predictive numerical models. \\ \hline [PERSON] et al. [80] & State-of-the-art, WSN & General applications, SHM network topology and deployments, hardware/software properties, communication protocols and standards; and energy harvesting solutions. \\ \hline [PERSON] et al. [81] & State-of-the-art, WSN & Integration of different types of sensors for SHM. \\ \hline [PERSON] et al. [82] & State-of-the-art, WSN & Qualitative and quantitative analysis of WSN requirements, accurate timing and synchronized sensing for high sampling rate sensors. \\ \hline [PERSON] et al. [83] & Sensors, ultrasonic probeheads, numerical models & Tests over a railway truss bridge. \\ \hline [PERSON] et al. [84] & Sensors & Development and experimental results of a liquid level sensor based on a fiber Bragg grating for monitoring differential settlement of railway track. \\ \hline [PERSON] et al. [85] & WSN, feature extraction & Analysis of the vibration patterns caused by trains passing by. \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Advanced services for the IoT-connected railways. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Service** & **Reference** & **Techniques** & **Main Contributions** \\ \hline \multirow{5}{*}{[PERSON] et al. [86]} & \multirow{2}{*}{Sensors, optical imaging, knowledge-based systems} & Monitor rail damage in the turnout zone. \\ \cline{2-3}  & & \\ \cline{1-1} \cline{2-3}  & [PERSON] et al. [87] & State-of-the-art Sensors, WSN & Review of network design for condition monitoring. \\ \cline{1-1} \cline{2-3}  & [PERSON] et al.