Integration of heterogeneous data sources and automated reasoning in healthcare and domotic IoT systems

In recent years, IoT technology has radically transformed many crucial industrial and service sectors such as healthcare. The multi-facets heterogeneity of the devices and the collected information provides important opportunities to develop innovative systems and services. However, the ubiquitous presence of data silos and the poor semantic interoperability in the IoT landscape constitute a significant obstacle in the pursuit of this goal. Moreover, achieving actionable knowledge from the collected data requires IoT information sources to be analysed using appropriate artificial intelligence techniques such as automated reasoning. In this thesis work, Semantic Web technologies have been investigated as an approach to address both the data integration and reasoning aspect in modern IoT systems. In particular, the contributions presented in this thesis are the following: (1) the IoT Fitness Ontology, an OWL ontology that has been developed in order to overcome the issue of data silos and enable semantic interoperability in the IoT fitness domain; (2) a Linked Open Data web portal for collecting and sharing IoT health datasets with the research community; (3) a novel methodology for embedding knowledge in rule-defined IoT smart home scenarios; and (4) a knowledge-based IoT home automation system that supports a seamless integration of heterogeneous devices and data sources.

Kinetic and mechanistic studies on peroxidation and stabilization of lipids with industrial and biological relevance

Lipid peroxidation is a complex mechanism that causes the degradation of lipid material of both industrial and biological significance. During processing, it is known that thermal stress produces oxidation and polymerization of oils. Additionally, biological lipids with both structural and bioactive roles are prone to peroxidation, which can have pathogenic effects including cancer and long-term degenerative disorders. To create innovative strategies to slow down the deterioration of lipids, it is crucial to improve our understanding of oxidation reactions and kinetics. To this purpose, Chapter II of this thesis focuses on the kinetic study of the oxidation reactions that take place during the thermal processing of bio-oils for industrial application. Through a new method it was possible to evaluate the kinetic parameters of oxidation of various lipid materials. This allowed us to distinguish between the different lipid materials based on their intrinsic properties. The effect of 18 antioxidants from the major families of natural and synthetic phenols were studied using the same methodology in order to acquire crucial data for enhancing the antioxidant activity of phenols based on structure-activity at high temperatures. Finally, it has been described how the antioxidant activity of α-tocopherol, revealed to be scarce in our conditions, can be improved in the presence of gamma-terpinene, through a synergistic action. Chapter III describes the synthesis and study of the antioxidant activity of polydopamine nanoparticles, in order to clarify the unclear mechanism of action of this material. Finally, in Chapter IV it was reported how the gamma-terpinene strongly inhibits the peroxidation of unsaturated lipids in heterogeneous model systems (micelles and liposomes) by forming hydroperoxyl radicals which diffuse outside the lipid nucleus, blocking the propagation of the chain radical. Furthermore, gamma-terpinene shows a very potent protective activity against ferroptosis being effective in the nanomolar range in the human neuroblastoma cell model.

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.

An architecture for network acceleration as a service in the cloud continuum

The pervasive availability of connected devices in any industrial and societal sector is pushing for an evolution of the well-established cloud computing model. The emerging paradigm of the cloud continuum embraces this decentralization trend and envisions virtualized computing resources physically located between traditional datacenters and data sources. By totally or partially executing closer to the network edge, applications can have quicker reactions to events, thus enabling advanced forms of automation and intelligence. However, these applications also induce new data-intensive workloads with low-latency constraints that require the adoption of specialized resources, such as high-performance communication options (e.g., RDMA, DPDK, XDP, etc.). Unfortunately, cloud providers still struggle to integrate these options into their infrastructures. That risks undermining the principle of generality that underlies the cloud computing scale economy by forcing developers to tailor their code to low-level APIs, non-standard programming models, and static execution environments. This thesis proposes a novel system architecture to empower cloud platforms across the whole cloud continuum with Network Acceleration as a Service (NAaaS). To provide commodity yet efficient access to acceleration, this architecture defines a layer of agnostic high-performance I/O APIs, exposed to applications and clearly separated from the heterogeneous protocols, interfaces, and hardware devices that implement it. A novel system component embodies this decoupling by offering a set of agnostic OS features to applications: memory management for zero-copy transfers, asynchronous I/O processing, and efficient packet scheduling. This thesis also explores the design space of the possible implementations of this architecture by proposing two reference middleware systems and by adopting them to support interactive use cases in the cloud continuum: a serverless platform and an Industry 4.0 scenario. A detailed discussion and a thorough performance evaluation demonstrate that the proposed architecture is suitable to enable the easy-to-use, flexible integration of modern network acceleration into next-generation cloud platforms.