# Towards the Internet of Smart Trains:
## 6 IoT-Enabled Services: From More Efficient Operations to New Business Models
### Energy Efficiency  
IIoT can play an important role to promote energy efficiency taking the EU environmental, financial and regulation concerns into account. Moreover, the techniques required for optimizing energy efficiency are strictly related to other solutions previously described for tackling issues like advanced asset monitoring. However, note that up to four energy-efficiency levels can be distinguished in railway scenarios [136]: energy-efficient driving, the coordination and re-scheduling of multiple trains in real time, the creation of energy-efficient timetables, and energy-saving planning. Regarding the coordination, [PERSON] et al. [136] propose an autonomous system to coordinate trains by optimizing the time spent between contiguous stations. Before departure, every train is able to determine the optimum running time by estimating when the preceding train will depart from the next station. Thus, the system has to achieve a good balance between energy efficiency and the time waited by the passengers. Energy efficiency can also be determined through smart metering methods. With a knowledge of the different consumers it is possible to perform an efficient energy management. Smart metering also allows for improving the management of assets and increases capacity. Three elements can be distinguished in smart metering systems: sensors, the communications between the different sensors, and the train-to-ground communications that require broadband links. For instance, wheel bearings can be monitored through WSNs [137]. In such a paper two issues are investigated experimentally: propagation and energy efficiency. First, the electromagnetic wave propagation characteristics around a train for high signal reliability. Measurements show that the path-loss exponent is different depending on the scenario. In general, the path-loss exponent is lower on top of the train than beside the train. The value of the path loss exponent with narrowband at 434 MHz is on average 3.67 with antennas are located under the train, compared to 2.27 on top. The communications were evaluated during a five-week field trial onboard a train in bad weather conditions. The number of messages transmitted successfully per day was in average about 92%. The lost messages were due to fading or mechanical damages of the sensors. Second, energy scavenging for minimum maintenance of the sensor network was investigated. The researchers verified that the sensors could be powered by solar power. However, a theoretical study indicates that the most suitable method to power the sensors is energy scavenging by vibration. The implementation of optimized train trajectories is also a topic under research. Speed profiles reduce energy consumption by avoiding running at reduced speed or unessential braking while arriving at planned times. An optimized train trajectory can be realized using a driver advisory system or Automatic Train Operation (ATO). Furthermore, the optimized trajectory needs input data, such as the train's position, gradient, direction, speed and maximum speed, dwell time, and station locations. In [138] it is proposed a genetic algorithm for optimizing the train speed profile. The results obtained following the advice generated by the DAS when updating the system every meter, showed that the optimized trajectory could save energy up to around 25%. However, a train positioning system error under 100 m increases the energy consumption by less than 0.3%, while an error under 500 m increases it by less than 1.5% for uphill lines; and 1.3% and 5.2%, respectively, for a downhill line. These results imply that it is sufficient to locate a train through a GPS to save energy and that, for such a purpose, it is not necessary to make use of high-precision positioning data. Further research includes dynamic DAS to recalculate an optimized trajectory when a correction of the train location occurs in real-time.