The digitalisation of freight rail is an essential improvement to create modern functions that offer a cost-effective, attractive service and improved operational opportunities to operators. These modern functions need intelligence, detection, actuation and communications. For this, generally, it is possible to process raw data in the Edge and send meaningful data over a communication link. However, the power supply is not granted in a freight wagon and so low power strategies need to be adopted. This paper presents the implementation and testing of a wireless connected heterogeneous multiprocessing architecture. From the power consumption point of view, this system has been stressed by means of a generic FFT function to evaluate the different on-board computing devices that have been decided. From the communication point of view, the LPWAN LoRa technology has been tested and validated on robustness and coverage. Thanks to the heterogeneous nature of this architecture and its configurability, it allows us to propose the most suitable computing ressources, data analysis and communication strategy in terms of efficiency and performance for the functions that this wagon on board unit needs to host and support. With this approach, operation data are reported to the centralised freight driver assistant system.

In this paper, we propose the design of a low-power wireless sensor network architecture that enables robust communications inside offshore wind turbines. This research work is in the scope of the WATEREYE EU Project, where we have designed a corrosion monitoring solution to work unattended. The architecture is composed of several fixed sensor nodes, one mobile sensor node, several anchors and the WATEREYE Computer (WEC). Our approach is based on Impulse Radio Ultra wideband (IR-UWB) technology offering reliable and low-power communications in these harsh environments. On top of that, we propose a double star network using two UWB channels for the following purposes: one network for communications to send the sensor data and another one for ranging estimations to calculate the indoor positioning of the mobile sensor node. The power strategies applied to our system, at Hardware (HW) and Firmware (FW) levels, are described in detail. Furthermore, we present power consumption measurements obtaining the power profiles and the autonomy of the most important components of the proposed architecture supplied by battery. On the other hand, we describe the methodology to analyze the range, reliability and continuity of the two UWB links providing the packet loss and gaps as a function of distance. The proposed communications system has been validated in three different scenarios considering two of them very hostile environments. Furthermore, one of the scenarios is a real offshore wind turbine.

In this paper, we propose a flexible Fog Computing architecture in which the main features are that it allows us to select among two different communication links (WiFi and LoRa) on the fly and offers a low-power solution, thanks to the applied power management strategies at hardware and firmware level. The proposed Fog Computing architecture is formed by sensor nodes and an Internet of Things (IoT) gateway. In the case of LoRa, we have the choice of implementing the LoRaWAN and Application servers on the cloud or on the IoT gateway, avoiding, in this case, to send data to the Cloud. Additionally, we have presented an specific setup and methodology with the aim of measuring the sensor node's power consumption and making sure there is a fair comparison between the different alternatives among the two selected wireless communication links by varying the duty cycle, the size of the payload, and the Spreading Factor (SF). This research work is in the scope of the STARPORTS Interconnecta Project, where we have deployed two sensor nodes in the offshore platform of PLOCAN, which communicate with the IoT gateway located in the PLOCAN premises. In this case, we have used LoRa communications due to the required large distance between the IoT gateway and the nodes in the offshore platform (in the range of kilometers). This deployment demonstrates that the proposed solution operates in a real environment and that it is a low-power and robust approach since it is sending data to the IoT gateway during more than one year and it continues working.