A complete low-power, low-cost and wireless solution for bridge structural health monitoring is presented. This work includes monitoring nodes with modular hardware design and low power consumption based on a control and resource management board called CoreBoard, and a specific board for sensorization called SensorBoard is presented. The firmware is presented as a design of FreeRTOS parallelised tasks that carry out the management of the hardware resources and implement the Random Decrement Technique to minimize the amount of data to be transmitted over the NB-IoT network in a secure way. The presented solution is validated through the characterization of its energy consumption, which guarantees an autonomy higher than 10 years with a daily 8 min monitoring periodicity, and two deployments in a pilot laboratory structure and the Eduardo Torroja bridge in Posadas (Córdoba, Spain). The results are compared with two different calibrated commercial systems, obtaining an error lower than 1.72% in modal analysis frequencies. The architecture and the results obtained place the presented design as a new solution in the state of the art and, thanks to its autonomy, low cost and the graphical device management interface presented, allow its deployment and integration in the current IoT paradigm.

A significant proportion of the world’s agricultural production is lost to pests and diseases. To mitigate this problem, an AIoT system for the early detection of pest and disease risks in crops is proposed. It presents a system based on low-power and low-cost sensor nodes that collect environmental data and transmit it once a day to a server via a NB-IoT network. In addition, the sensor nodes use individual, retrainable and updatable machine learning algorithms to assess the risk level in the crop every 30 min. If a risk is detected, environmental data and the risk level are immediately sent. Additionally, the system enables two types of notification: email and flashing LED, providing online and offline risk notifications. As a result, the system was deployed in a real-world environment and the power consumption of the sensor nodes was characterized, validating their longevity and the correct functioning of the risk detection algorithms. This allows the farmer to know the status of their crop and to take early action to address these threats.

The rise of Internet of Things (IoT) systems and the evolution of Low-Power Wide-Area Networks have directly contributed to the emergence of a new generation of Structural Health Monitoring (SHM) systems based on the Non-Destructive Test approach. Consequently, this article presents the design and development of a synchronous, low-cost, real-time, wireless, and low-power consumption SHM system for pre-existing buildings and infrastructures, which has been validated on a structure built in the High Middle Ages forming part of the historical heritage of the city of Seville (Spain). The system proposes a modular and scalable design with the capacity for synchronous monitoring of the accelerations of a structure thanks to the deployment of several reduced-size nodes that acquire and transmit the accelerations of the structures through a Secure Sockets Layer (SSL) NB-IoT connection. This aspect and Firmware Over The Air (FOTA) capability enables the permanent deployment of sensor nodes, thereby not only obviating the use of costly traditional devices but also granting access to the structures to be monitored for each test and maintenance task and hence supporting the tasks related to the preventive conservation of heritage. The experimental tests carried out demonstrate the low impact of GPS time synchronization and FOTA on the autonomy of the system. The precision of the system is also validated by comparing the results with a precision system in a real field test. Furthermore, continuous monitoring is guaranteed through the graphical interface developed as a composition of microservices, which enables management of the deployed networks.

Particle detector systems require data acquisition systems (DAQs) as their back-end. This paper presents a new edge-computing DAQ that is capable of handling multiple pixel detectors simultaneously and was designed for particle-tracking experiments. The system was designed for the ROC4SENS readout chip, but its control logic can be adapted for other pixel detectors. The DAQ was based on a system-on-chip FPGA (SoC FPGA), which includes an embedded microprocessor running a fully functional Linux system. An application using a client–server architecture was developed to facilitate remote control and data visualization. The comprehensive DAQ is very compact, thus reducing the typical hardware load in particle tracking experiments, especially during the obligatory characterization of particle telescopes.

This paper presents a prototype of an architecture of a cyber-physical system (CPS) for extending the lifetime of electric vehicle batteries. The proposed solution features a secure, AUTOSAR-compliant architecture with wireless connection designed to facilitate advanced optimization techniques for battery usage. Central to this architecture is the integration of a cloud-based complex Digital Twin - built upon high-precision behavioural models - and the deployment on the vehicle of corresponding simplified models for edge computing. This enables real-time estimation of battery status and optimal usage parameters locally, without relying on continuous cloud connectivity. To address performance degradation over time, the system incorporates a methodology for remote, wireless retraining of edge models using the cloud Digital Twin via 5G communication. The proposed solution has been fully implemented and validated in laboratory using a real battery yielding State-of-Charge (SoC) predictions with an error margin below 3.5%. Additionally, behavioural models were also developed for State-of-Health (SoH), Remaining Useful Life (RUL), State-of-Security (SoS) and State-of-Power (SoP). These models also provide, in a second iteration analysis, values for the maximum current to demand from the battery and the optimal operating temperature. Collectively, the proposed CPS architecture enables advanced battery management with a positive impact in the battery lifetime.