Recently, the interest around the Blockchain concept has grown faster and, as a consequence, several studies about the possibility of exploiting such technology in different application domains have been conducted. Most of these studies highlighted the benefits that the use of the blockchain could bring in those contexts where integrity and authenticity of the data are important, e.g., for reasons linked to regulations about consumers’ healthcare. In such cases, it would be important to collect data, coming in real-time through sensors, and then store them in the blockchain, so that they can become immutable and tamper-proof. In this paper, the design and development of a software framework that allows Internet-of-Things (IoT) devices to interact directly with an Ethereum-based blockchain are reported. The proposed solution represents an alternative way for integrating a wide category of IoT devices without relying on a centralized intermediary and third-party services. The main application scenario for which the project has been conceived regards food-chain traceability in the Industry 4.0 domain. Indeed, the designed system has been integrated into the depiction of a use case for monitoring the temperature of fish products within a warehouse and during the delivery process.

Air quality, especially particulate matter, has recently attracted a lot of attention from governments, industry, and academia, motivating the use of denser air quality monitoring networks based on low-cost sensing strategies. However, low-cost sensors are frequently sensitive to aging, environmental conditions, and pollutant cross-sensitivities. These issues have been only partially addressed, limiting their usage. In this study, we develop a low-cost particulate matter monitoring system based on special-purpose acquisition boards, deployed for monitoring air quality on both stationary and mobile sensor platforms. We explore the influence of all model variables, the quality of different calibration strategies, the accuracy across different concentration ranges, and the usefulness of redundant sensors placed in each station. The collected sensor data amounts to about 50GB of data, gathered in six months during the winter season. Tests of statically immovable stations include an analysis of accuracy and sensors reliability made by comparing our results with more accurate and expensive standard - radiation sensors. Tests on mobile stations have been designed to analyze the reactivity of our system to unexpected and abrupt events. These experiments embrace traffic analysis, pollution investigation using different means of transport and pollution analysis during peculiar events. With respect to other approaches, our methodology has been proved to be extremely easy to calibrate, to offer a very high sample rate (one sample per second), and to be based on an open-source software architecture. Database and software are available as open source in [1].

Credit risk analysis (CRA) quantum algorithms aim at providing a quadratic speedup over classical analogous methods. Despite this, experts in the business domain have identified significant limitations in the existing approaches. Thus, we proposed a new variant of the CRA quantum algorithm to address these limitations. In particular, we improved the risk model for each asset in a portfolio by enabling it to consider multiple systemic risk factors, resulting in a more realistic and complex model for each asset’s default probability. Additionally, we increased the flexibility of the loss-given-default input by removing the constraint of using only integer values, enabling the use of real data from the financial sector to establish fair benchmarking protocols. Furthermore, all proposed enhancements were tested both through classical simulation of quantum hardware and, for this new version of our work, also using QPUs from IBM Quantum Experience in order to provide a baseline for future research. Our proposed variant of the CRA quantum algorithm addresses the significant limitations of the current approach and highlights an increased cost in terms of circuit depth and width. In addition, it provides a path to a substantially more realistic software solution. Indeed, as quantum technology progresses, the proposed improvements will enable meaningful scales and useful results for the financial sector.

The Indoor Environmental Quality (IEQ) combines thermal, visual, acoustic, and air-quality conditions in indoor environments and affects occupants’ health, well-being, and comfort. Performing continuous monitoring to assess IEQ is increasingly proving to be important, also due to the large amount of time that people spend in closed spaces. In the present study, the design, development, and metrological characterization of a low-cost multi-sensor device is presented. The device is part of a wider system, hereafter referred to as PROMET&O (PROactive Monitoring for indoor EnvironmenTal quality & cOmfort), that also includes a questionnaire for the collection of occupants’ feedback on comfort perception and a dashboard to show end users all monitored data. The PROMET&O multi-sensor monitors the quality conditions of indoor environments thanks to a set of low-cost sensors that measure air temperature, relative humidity, illuminance, sound pressure level, carbon monoxide, carbon dioxide, nitrogen dioxide, particulate matter, volatile organic compounds, and formaldehyde. The device architecture is described, and the design criteria related to measurement requirements are highlighted. Particular attention is paid to the calibration of the device to ensure the metrological traceability of the measurements. Calibration procedures, based on the comparison to reference standards and following commonly employed or ad hoc developed technical procedures, were defined and applied to the bare sensors of air temperature and relative humidity, carbon dioxide, illuminance, sound pressure level, particulate matter, and formaldehyde. The next calibration phase in the laboratory will be aimed at analyzing the mutual influences of the assembled multi-sensor hardware components and refining the calibration functions.

The rapid spread of Internet of Things technologies has enabled a continuous monitoring of indoor environmental quality in office environments by integrating monitoring devices equipped with low-cost sensors and cloud platforms for data storage and visualization. Critical aspects in the development of such monitoring systems are effective data acquisition, processing, and visualization strategies, which significantly influence the performance of the system both at monitoring device and at cloud platform level. This paper proposes novel strategies to address the challenges in the design of a complete monitoring system for indoor environmental quality. By adopting the proposed solution, one can reduce the data rate transfer between the monitoring devices and the server without loss of information, as well as achieve efficient data storage and aggregation on the server side to minimize retrieval times. Finally, enhanced flexibility in the dashboard for data visualization is obtained, thus enabling graph modifications without extensive coding efforts. The functionality of the developed system was assessed, with the collected data in good agreement with those from other instruments used as references.