The development of Internet of Things (IoT) systems is a rapidly evolving scenario, thanks also to newly available low-power wide area network (LPWAN) technologies that are utilized for environmental monitoring purposes and to prevent potentially dangerous situations with smaller and less expensive physical structures. This paper presents the design, implementation and test results of a flood-monitoring system based on LoRa technology, tested in a real-world scenario. The entire system is designed in a modular perspective, in order to have the capability to interface different types of sensors without the need for making significant hardware changes to the proposed node architecture. The information is stored through a device equipped with sensors and a microcontroller, connected to a LoRa wireless module for sending data, which are then processed and stored through a web structure where the alarm function is implemented in case of flooding.

It is necessary to ensure safety in terms of health and accidents through the real-time monitoring of the construction site environment and workers. This problem has become of great importance due to the economic and social implications. Therefore, a sensor-based approach has been found to be beneficial in Building Information Modeling (BIM). Wireless Sensor Network (WSN) technologies are well-suited for the deployment of monitoring systems. A suitable technical solution for node communication in a WSN is Long Range (LoRa) modulation technology. In this study, an autonomous LoRa-based system for the monitoring of a construction site in Lungro, Calabria, Italy, is presented. The spatial monitoring of working personnel is achieved by employing a tracker device with an Inertial Measurement Unit (IMU) and a Global Positioning System (GPS) device. Accesses of personnel and gear to the site are registered using Radio Frequency Identification (RFID) tags equipped with protective gear. Fixed-position solar-powered sensor nodes are also employed for structural monitoring, i.e., movement sensors are used to monitor the variation of scaffolding, building structures, and under-work housing inclinations. Long Range Wide Area Network (LoRaWAN) gateways interface with the nodes and the internet for data exchange, enabling an Internet of Things (IoT) paradigm for the monitoring solution. A comprehensive overview of the workers and structural nodes, along with the RFID access management system and LoRaWAN gateway features, is provided in this article. A description of the web interface is also reported.

Rockfalls and landslides are hazards triggered from geomorphological and climatic factors other than human interaction. The economic and social impacts are not negligible, therefore the topic has become an important field in the application of remote monitoring. Wireless sensor networks (WSNs) are particularly suited for the deployment of such systems, thanks to the different technologies and topologies that are evolving nowadays. Among these, LoRa modulation technique represents a fitting technical solution for nodes communication in a WSN. In this paper, a smart autonomous LoRa-based rockfall and landslide monitoring system is presented. The structure has been operating in Pantelleria Island, Sicily, Italy. The sensing elements are disposed in sensor nodes arranged in a star topology. Network access to the LoRaWAN and the Internet is provided through gateways using a portable, solar powered device assembly. A system overview concerning both hardware and functionality of the nodes and gateways devices, then a power analysis is reported, and a monthly recorded result is presented, with related discussion.

Nowadays, the integration of smart systems within the modern industrial scenario is a continuously growing paradigm. Computer Numerical Control (CNC) machinery can heavily benefit from the introduction of Artificial Intelligence (AI) based monitoring applications. In this paper, we present an industrial condition and fault prevention monitoring system for CNC tools. The developed system is the result of an industrial project aimed at realizing a multi-purpose machine which is currently in pre-commercial stage. The results of this work represent the base platform for the further commercial development, which will be carried on from the industrial partners in accordance with clients feedbacks and specifications. This work presents the hardware architecture of the system, the web-based monitoring platform for remote management, and the AI framework used for fault monitoring. The multi-purpose machine is equipped with accelerometer units to monitor the vibration in multiple points of the structure. The control unit of the machine is connected to the sensing nodes and is used to communicate the actual machine state to a remote web platform. The accelerometric data are analyzed through an AI algorithm to perform fault detection. The fault detection algorithm was trained with the measurements performed on the machine under controlled environment faulty operation. The Internet of Things (IoT) based architecture has proven to be effective to facilitate the supervision of the machining processes, and the AI-based classification shows good classification performances for the fault detection tests.

Rockfalls and landslide events are caused by different factors among which are included geomorphological and climatic factors and also human interaction. Therefore, the economic and social impacts can be significant and the remote monitoring of such hazards has become an essential topic in various applications. Wireless sensor networks (WSNs) are well suited for the deployment of monitoring systems, benefiting from the different technologies and topologies that are available and evolving nowadays. This review paper aims to summarize and overview the up-to-date state of the art of rockfall and landslide monitoring systems based on WSNs. The implementation and methods were analyzed for each solution, along with the system architecture and relevant hardware aspects. All the retrieved data were used to analyze the current trends and future possibilities in the field of WSN geohazard monitoring.

In this paper, a new realization of electronically tunable voltage output second-order low-pass (LP) and band-pass (BP) filter is presented. The circuit has a multiple-input single-output structure, and LP and BP outputs are provided using the same structure. One electronically variable second-generation voltage conveyor (VCII), whose impedance at the Y port can be electronically varied using a control current (I_con), two capacitors, and one resistor are used. By changing the value of I_con, the impedance value at the Y port can be electronically varied; therefore, the value of ω₀ can be tuned. This feature helps to reduce the number of passive components used. Interestingly, the LP and BP outputs are provided at the low-impedance Z port of the VCII, and there is no need for an extra voltage buffer for practical use. The circuit enjoys a simple realization consisting of only 24 MOS transistors. Simulation results using PSpice and 0.18 μm CMOS parameters are provided. The value of ω₀ can be varied from 1.2 MHz to 1.7 MHz, while I_con varies from 0 to 50 µA, with a power consumption variation from 244 µW to 515 µW.