Edge and Big Data technologies for Industry 4.0 to create an integrated pre-sale and after-sale environment

The fourth industrial revolution, also known as Industry 4.0, has rapidly gained traction in businesses across Europe and the world, becoming a central theme in small, medium, and large enterprises alike. This new paradigm shifts the focus from locally-based and barely automated firms to a globally interconnected industrial sector, stimulating economic growth and productivity, and supporting the upskilling and reskilling of employees. However, despite the maturity and scalability of information and cloud technologies, the support systems already present in the machine field are often outdated and lack the necessary security, access control, and advanced communication capabilities. This dissertation proposes architectures and technologies designed to bridge the gap between Operational and Information Technology, in a manner that is non-disruptive, efficient, and scalable. The proposal presents cloud-enabled data-gathering architectures that make use of the newest IT and networking technologies to achieve the desired quality of service and non-functional properties. By harnessing industrial and business data, processes can be optimized even before product sale, while the integrated environment enhances data exchange for post-sale support. The architectures have been tested and have shown encouraging performance results, providing a promising solution for companies looking to embrace Industry 4.0, enhance their operational capabilities, and prepare themselves for the upcoming fifth human-centric revolution.

Studio e sviluppo di sistemi RFID in banda UHF

The recent widespread diffusion of radio-frequency identification (RFID) applications operating in the UHF band has been supported by both the request for greater interrogation ranges and greater and faster data exchange. UHF-RFID systems, exploiting a physical interaction based on Electromagnetic propagation, introduce many problems that have not been fully explored for the previous generations of RFID systems (e.g. HF). Therefore, the availability of reliable tools for modeling and evaluating the radio-communication between Reader and Tag within an RFID radio-link are needed. The first part of the thesis discuss the impact of real environment on system performance. In particular an analytical closed form formulation for the back-scattered field from the Tag antenna and the formulation for the lower bound of the BER achievable at the Reader side will be presented, considering different possible electromagnetic impairments. By means of the previous formulations, of the analysis of the RFID link operating in near filed conditions and of some electromagnetic/system-level co-simulations, an in-depth study of the dimensioning parameters and the actual performance of the systems will be discussed and analyzed, showing some relevant properties and trade-offs in transponder and reader design. Moreover a new low cost approach to extend the read range of the RFID UHF passive systems will be discussed. Within the scope to check the reliability of the analysis approaches and of innovative proposals, some reference transponder antennas have been designed and extensive measurement campaign has been carried out with satisfactory results. Finally, some commercial ad-hoc transponder for industrial application have been designed within the cooperation with Datalogic s.p.a., some guidelines and results will be briefly presented.

Efficient wireless coverage of in-building environments with low electromagnetic impact

The city of tomorrow is a major integrating stake, which crosses a set of major broad spectrum domains. One of these areas is the instrumentation of this city and the ubiquity of the exchange of data, which will give the pulse of this city (sensors) and its breathing in a hyper-connected world within indoor and outdoor dense areas (data exchange, 5G and 6G). Within this context, the proposed doctorate project has the objective to realize cost- and energy- effective, short-range communication systems for the capillary wireless coverage of in-door environments with low electromagnetic impact and for highly dense outdoor networks. The result will be reached through the combined use of: 1) Radio over Fiber (RoF) Technology, to bring the Radio Frequency (RF) signal to the different areas to be covered. 2) Beamforming antennas to send in real time the RF power just in the direction(s) where it is really necessary.

Application Platforms for the Internet of Things: Theory, Architecture, Protocols, Data Formats, and Privacy

The Internet of Things (IoT) is the next industrial revolution: we will interact naturally with real and virtual devices as a key part of our daily life. This technology shift is expected to be greater than the Web and Mobile combined. As extremely different technologies are needed to build connected devices, the Internet of Things field is a junction between electronics, telecommunications and software engineering. Internet of Things application development happens in silos, often using proprietary and closed communication protocols. There is the common belief that only if we can solve the interoperability problem we can have a real Internet of Things. After a deep analysis of the IoT protocols, we identified a set of primitives for IoT applications. We argue that each IoT protocol can be expressed in term of those primitives, thus solving the interoperability problem at the application protocol level. Moreover, the primitives are network and transport independent and make no assumption in that regard. This dissertation presents our implementation of an IoT platform: the Ponte project. Privacy issues follows the rise of the Internet of Things: it is clear that the IoT must ensure resilience to attacks, data authentication, access control and client privacy. We argue that it is not possible to solve the privacy issue without solving the interoperability problem: enforcing privacy rules implies the need to limit and filter the data delivery process. However, filtering data require knowledge of how the format and the semantics of the data: after an analysis of the possible data formats and representations for the IoT, we identify JSON-LD and the Semantic Web as the best solution for IoT applications. Then, this dissertation present our approach to increase the throughput of filtering semantic data by a factor of ten.

How do Firms ask for Consumers’ Data Permission? The Value of Companies Data Practices.

On May 25, 2018, the EU introduced the General Data Protection Regulation (GDPR) that offers EU citizens a shelter for their personal information by requesting companies to explain how people’s information is used clearly. To comply with the new law, European and non-European companies interacting with EU citizens undertook a massive data re-permission-request campaign. However, if on the one side the EU Regulator was particularly specific in defining the conditions to get customers’ data access, on the other side, it did not specify how the communication between firms and consumers should be designed. This has left firms free to develop their re-permission emails as they liked, plausibly coupling the informative nature of these privacy-related communications with other persuasive techniques to maximize data disclosure. Consequently, we took advantage of this colossal wave of simultaneous requests to provide insights into two issues. Firstly, we investigate how companies across industries and countries chose to frame their requests. Secondly, we investigate which are the factors that influenced the selection of alternative re-permission formats. In order to achieve these goals, we examine the content of a sample of 1506 re-permission emails sent by 1396 firms worldwide, and we identify the dominant “themes” characterizing these emails. We then relate these themes to both the expected benefits firms may derive from data usage and the possible risks they may experience from not being completely compliant to the spirit of the law. Our results show that: (1) most firms enriched their re-permission messages with persuasive arguments aiming at increasing consumers’ likelihood of relinquishing their data; (2) the use of persuasion is the outcome of a difficult tradeoff between costs and benefits; (3) most companies acted in their self-interest and “gamed the system”. Our results have important implications for policymakers, managers, and customers of the online sector.

Data-driven and data-oriented methods for materials science and technologies

The discovery of new materials and their functions has always been a fundamental component of technological progress. Nowadays, the quest for new materials is stronger than ever: sustainability, medicine, robotics and electronics are all key assets which depend on the ability to create specifically tailored materials. However, designing materials with desired properties is a difficult task, and the complexity of the discipline makes it difficult to identify general criteria. While scientists developed a set of best practices (often based on experience and expertise), this is still a trial-and-error process. This becomes even more complex when dealing with advanced functional materials. Their properties depend on structural and morphological features, which in turn depend on fabrication procedures and environment, and subtle alterations leads to dramatically different results. Because of this, materials modeling and design is one of the most prolific research fields. Many techniques and instruments are continuously developed to enable new possibilities, both in the experimental and computational realms. Scientists strive to enforce cutting-edge technologies in order to make progress. However, the field is strongly affected by unorganized file management, proliferation of custom data formats and storage procedures, both in experimental and computational research. Results are difficult to find, interpret and re-use, and a huge amount of time is spent interpreting and re-organizing data. This also strongly limit the application of data-driven and machine learning techniques. This work introduces possible solutions to the problems described above. Specifically, it talks about developing features for specific classes of advanced materials and use them to train machine learning models and accelerate computational predictions for molecular compounds; developing method for organizing non homogeneous materials data; automate the process of using devices simulations to train machine learning models; dealing with scattered experimental data and use them to discover new patterns.