# Towards the Internet of Smart Trains:
## 6 IoT-Enabled Services: From More Efficient Operations to New Business Models
### From Reactive to Predictive Maintenance  
It is expected that maintenance costs will rise in the next years due to the aging of the infrastructure and the increasing number of passengers and freight transported. Due to this trend, there is a demand for monitoring complex maintenance operations related to the different elements that conform the railway system [64]. Maintenance decisions about critical items of infrastructures can be improved by using the precise location of the train, its speed, weight, data from vibration sensors located alongside the track, weather reports, and details on how long the power connector is disconnected from the catenary during operations [65]. The fusion of this information with other meta-data, such as catenary dilation factors and track temperature, can further enhance the decision-making process and help to create more sophisticated rail scheduling software. For instance, [PERSON] et al. [66] studied how to monitor the state of light rail vehicles and their tracks. The researchers made use of sensors embedded into the axle boxes to adapt dynamically maintenance requirements and speed limits. For instance, in [67] it is analyzed how to schedule preventive maintenance depending on different strategies. Furthermore, in [68] the authors studied the application of big data techniques to make decisions on the maintenance of railway tracks. A similar approach is described in [69; 70], where the authors propose the use of an expert and a Decision Support System (DSS) to plan and schedule different common rail activities. In recent years, ballasted tracks have been replaced by systems based on slabs, which are more secure and sustainable in high-speed railways. Preventive maintenance can be carried out in such infrastructures by embedding sensors that track movements and vibrations [71; 72]. The design of such systems has to consider large areas in remote places that have no Internet access or electricity. In such scenarios the information collected is sent to passing trains that transmit the data later to a Remote Control Center (RCC). Another research article focused on the evaluation of the influence of train vibrations is presented in [73]. In the case of [74], the system proposed includes threat detection. For instance, [PERSON] et al. [75] present a shape-based method for analyzing the normalized electric current patterns in Railway Point Machines (consisting in a motor, reduction gear, several bearings, derive-detection rods, and switches) in order to detect the replacement conditions with acceptable accuracy. [PERSON] et al. [76] describe the benefits of using predictive control systems to monitor different activities. Such a kind of systems is used for making better decisions when determining maintenance procedures. Specifically, there is a type of algorithms called Model Predictive Control (MPC) that has been devised explicitly for monitoring actions related to certain assets in order to anticipate events. The complexity of the vehicle dynamics usually involves using approaches like extended Kalman filters, which are able to estimate the dynamic performance of certain elements as the train moves, or particle filtering, which can assess the state in non-linear non-Gaussian scenarios in order to detect imminent faults. Furthermore, [PERSON] et al. point out that Wheelset Condition Monitoring (WCM) systems can be applied to estimate the deterioration of the wheels through different sensors and automatic identification systems. Finally, the authors conclude that, in order to monitor the conditions in high-performance real-time scenarios, it is required to make use of WCMs and simulation techniques. Another example of DSS is presented in [77]. [PERSON] et al. propose a smart tool that helps railway infrastructure designers to create more secure and more efficient electrification systems. The tool prevents dangerous situations associated with errors during the design stage.