In recent years, the growing number of vehicles on the road have exacerbated issues related to safety and traffic congestion. However, the advent of the Internet of Vehicles (IoV) holds the potential to transform mobility, enhance traffic management and safety, and create smarter, more interconnected road networks. This paper addresses key road safety concerns, focusing on driver condition detection, vehicle monitoring, and traffic and road management. Specifically, various models proposed in the literature for monitoring the driver’s health and detecting anomalies, drowsiness, and impairment due to alcohol consumption are illustrated. The paper describes vehicle condition monitoring architectures, including diagnostic solutions for identifying anomalies, malfunctions, and instability while driving on slippery or wet roads. It also covers systems for classifying driving style, as well as tire and emissions monitoring. Moreover, the paper provides a detailed overview of the proposed traffic monitoring and management solutions, along with systems for monitoring road and environmental conditions, including the sensors used and the Machine Learning (ML) algorithms implemented. Finally, this review also presents an overview of innovative commercial solutions, illustrating advanced devices for driver monitoring, vehicle condition assessment, and traffic and road management.

The Internet of Things (IoT) has radically changed the industrial world, enabling the integration of numerous systems and devices into the industrial ecosystem. There are many areas of the manufacturing industry in which IoT has contributed, including plants’ remote monitoring and control, energy efficiency, more efficient resources management, and cost reduction, paving the way for smart manufacturing in the framework of Industry 4.0. This review article provides an up-to-date overview of IoT systems and machine learning (ML) algorithms applied to smart manufacturing (SM), analyzing four main application fields: security, predictive maintenance, process control, and additive manufacturing. In addition, the paper presents a descriptive and comparative overview of ML algorithms mainly used in smart manufacturing. Furthermore, for each discussed topic, a deep comparative analysis of the recent IoT solutions reported in the scientific literature is introduced, dwelling on the architectural aspects, sensing solutions, implemented data analysis strategies, communication tools, performance, and other characteristic parameters. This comparison highlights the strengths and weaknesses of each discussed solution. Finally, the presented work outlines the features and functionalities of future IoT-based systems for smart industry applications.

The roads we travel daily are exposed to several energy sources (mechanical load, solar radiation, heat, air movement, etc.), which can be exploited to make common systems and apparatus for roadways (i.e., lighting, video surveillance, and traffic monitoring systems) energetically autonomous. For decades, research groups have developed many technologies able to scavenge energy from the said sources related to roadways: electromagnetism, piezoelectric and triboelectric harvesters for the cars’ stress and vibrations, photovoltaic modules for sunlight, thermoelectric solutions and pyroelectric materials for heat and wind turbines optimized for low-speed winds, such as the ones produced by moving vehicles. Thus, this paper explores the existing technologies for scavenging energy from sources available on roadways, both natural and related to vehicular transit. At first, to contextualize them within the application scenario, the available energy sources and transduction mechanisms were identified and described, arguing the main requirements that must be considered for developing harvesters applicable on roadways. Afterward, an overview of energy harvesting solutions presented in the scientific literature to recover energy from roadways is introduced, classifying them according to the transduction method (i.e., piezoelectric, triboelectric, electromagnetic, photovoltaic, etc.) and proposed system architecture. Later, a survey of commercial systems available on the market for scavenging energy from roadways is introduced, focusing on their architecture, performance, and installation methods. Lastly, comparative analyses are offered for each device category (i.e., scientific works and commercial products), providing insights to identify the most promising solutions and technologies for developing future self-sustainable smart roads.

This paper describes the development and characterization of a smart garment for monitoring the environmental and biophysical parameters of the user wearing it; the wearable application is focused on the control to workers’ conditions in dangerous workplaces in order to prevent or reduce the consequences of accidents. The smart jacket includes flexible solar panels, thermoelectric generators and flexible piezoelectric harvesters to scavenge energy from the human body, thus ensuring the energy autonomy of the employed sensors and electronic boards. The hardware and firmware optimization allowed the correct interfacing of the heart rate and SpO₂ sensor, accelerometers, temperature and electrochemical gas sensors with a modified Arduino Pro mini board. The latter stores and processes the sensor data and, in the event of abnormal parameters, sends an alarm to a cloud database, allowing company managers to check them via a web app. The characterization of the harvesting subsection has shown that ≈ 265 mW maximum power can be obtained in a real scenario, whereas the power consumption due to the acquisition, processing and BLE data transmission functions determined that a 10 mAh/day charge is required to ensure the device’s proper operation. By charging a 380 mAh Lipo battery in a few hours by means of the harvesting system, an energy autonomy of 23 days was obtained, in the absence of any further energy contribution.