Natural disasters cause enormous damage and losses every year, both economic and in terms of human lives. It is essential to develop systems to predict disasters and to generate and disseminate timely warnings. Recently, technologies such as the Internet of Things solutions have been integrated into alert systems to provide an effective method to gather environmental data and produce alerts. This work reviews the literature regarding Internet of Things solutions in the field of Early Warning for different natural disasters: floods, earthquakes, tsunamis, and landslides. The aim of the paper is to describe the adopted IoT architectures, define the constraints and the requirements of an Early Warning system, and systematically determine which are the most used solutions in the four use cases examined. This review also highlights the main gaps in literature and provides suggestions to satisfy the requirements for each use case based on the articles and solutions reviewed, particularly stressing the advantages of integrating a Fog/Edge layer in the developed IoT architectures.

Rivers close to populated or strategically important areas can cause damages and safety risks to people in the event of a flood. Traditional river flood monitoring systems like radar and ultrasonic sensors may not be completely reliable and require frequent on-site human interventions for calibration. This time-consuming and resource-intensive activity has attracted the attention of many researchers looking for highly reliable camera-based solutions. In this article we propose an automatic Computer Vision solution for river’s water-level monitoring, based on the processing of staff gauge images acquired by a V-IoT device. The solution is based on two modules. The first is implemented on the edge in order to avoid power consumption due to the transmission of poor quality frames, and another is implemented on the Cloud server, where the frames acquired and sent by the V-IoT device are processed for water level extraction. The proposed system was tested on sample images relating to more than a year of acquisitions at a river site. The first module of the proposed solution achieved excellent performances in discerning bad quality frames from good quality ones. The second module achieved very good results too, especially for what it concerns night frames.

This paper presents an IoT architecture for continuous and real-time building monitoring. The proposed solution is based on a sensor node positioned inside the building to be monitored, connected to the Internet, able to perform continuous measurements and to real-time send raw data to a remote server through the MQTT protocol. Thanks to the development of a specific Web application, each authorized user can access the real-time information on the health status of the structure, the history of the measurements and, if necessary, to send commands to the sensor node. The goal is to provide a low-cost, reliable and replicable system for the implementation of a widespread sensors network for structural monitoring.

Customers are asking more and more customized products and expect to receive them in really short times. That is only reachable if there is horizontal and vertical integration, together with high information availability and transparency inside a company. When the production is not fully automatized, i.e. in those companies where the assembly or the production still relies on manual work of people, the monitoring of the line production, in terms of number of pieces produced, may be tricky due to the inevitable variability that operators add to the process, thus making essential the creation of smart systems able to deal with such complex environments and autonomously monitor them. Computer vision systems can be customized and very smart in various contexts, if properly modeled. In this paper we are going to describe the developed Machine Vision algorithm that, building upon BLOB (Binary Large Objects) analysis, is able to detect and count objects produced in a complex industrial context of manual assembly. The developed algorithm would then be compared to a more robust one taken as reference point for a comparison. The comparison between our algorithm and the Machine Learning-based Detector aims at showing the comparable Accuracy, Specificity and Sensitivity of our method, together with its higher versatility and processing speed, thus making it applicable in the plant-wide real-time monitoring of the manual assembly lines.

The rapid worldwide aging makes necessary the development of advanced technologies for the objective identification of diseases and disabilities onset. Accordingly, we need to regard the biological age as an alternative and more reliable indicator of the physiological decline of individuals, respect to the simple chronological age. In this paper, we present an eHealth system for estimate the biological age of elderly people starting from the assessment of the frailty phenotype. The frailty phenotype evaluation is made possible using a standard protocol for data acquisition and a cloud application for processing and storing data. The proposed eHealth system is also equipped with wireless, small and non-invasive wearable sensors for an objective evaluation of the mobility of a subject. The eHealth system is tested on a reference population in order to have an amount of data necessary for defining a model to estimate the biological age. The use of the presented system on a reference population, and the availability of data regarding their mobility, allow the validation of the proposed model for the computation of the biological age via simple and objective frailty phenotype assessment.

Maintenance cost is among the highest operational expenses for manufacturing firms. Proper scheduling of maintenance intervention results in optimized equipment life utilization, higher product quality, and reduced costs. For Cartesian Robot’s accuracy and precision it is important that the belt is well calibrated. Nonetheless, the manual assessment of calibration requires to stop the robot, which in turns causes the stop of the production with related consequences. In this work we are going to develop a Machine Learning based Classification Model, able to use cycle current consumption data of a Cartesian Robot in order to understand if the drive belt is calibrated or not. The trained model will be tested on completely new data whose label is known, to ensure its reliability.