Wireless systems for the fourth generation

Today, third generation networks are consolidated realities, and user expectations on new applications and services are becoming higher and higher. Therefore, new systems and technologies are necessary to move towards the market needs and the user requirements. This has driven the development of fourth generation networks. ”Wireless network for the fourth generation” is the expression used to describe the next step in wireless communications. There is no formal definition for what these fourth generation networks are; however, we can say that the next generation networks will be based on the coexistence of heterogeneous networks, on the integration with the existing radio access network (e.g. GPRS, UMTS, WIFI, ...) and, in particular, on new emerging architectures that are obtaining more and more relevance, as Wireless Ad Hoc and Sensor Networks (WASN). Thanks to their characteristics, fourth generation wireless systems will be able to offer custom-made solutions and applications personalized according to the user requirements; they will offer all types of services at an affordable cost, and solutions characterized by flexibility, scalability and reconfigurability. This PhD’s work has been focused on WASNs, autoconfiguring networks which are not based on a fixed infrastructure, but are characterized by being infrastructure less, where devices have to automatically generate the network in the initial phase, and maintain it through reconfiguration procedures (if nodes’ mobility, or energy drain, etc..., cause disconnections). The main part of the PhD activity has been focused on an analytical study on connectivity models for wireless ad hoc and sensor networks, nevertheless a small part of my work was experimental. Anyway, both the theoretical and experimental activities have had a common aim, related to the performance evaluation of WASNs. Concerning the theoretical analysis, the objective of the connectivity studies has been the evaluation of models for the interference estimation. This is due to the fact that interference is the most important performance degradation cause in WASNs. As a consequence, is very important to find an accurate model that allows its investigation, and I’ve tried to obtain a model the most realistic and general as possible, in particular for the evaluation of the interference coming from bounded interfering areas (i.e. a WiFi hot spot, a wireless covered research laboratory, ...). On the other hand, the experimental activity has led to Throughput and Packet Error Rare measurements on a real IEEE802.15.4 Wireless Sensor Network.

Simulation of Smart Materials

The aim of this thesis is the elucidation of structure-properties relationship of molecular semiconductors for electronic devices. This involves the use of a comprehensive set of simulation techniques, ranging from quantum-mechanical to numerical stochastic methods, and also the development of ad-hoc computational tools. In more detail, the research activity regarded two main topics: the study of electronic properties and structural behaviour of liquid crystalline (LC) materials based on functionalised oligo(p-phenyleneethynylene) (OPE), and the investigation on the electric field effect associated to OFET operation on pentacene thin film stability. In this dissertation, a novel family of substituted OPE liquid crystals with applications in stimuli-responsive materials is presented. In more detail, simulations can not only provide evidence for the characterization of the liquid crystalline phases of different OPEs, but elucidate the role of charge transfer states in donor-acceptor LCs containing an endohedral metallofullerene moiety. Such systems can be regarded as promising candidates for organic photovoltaics. Furthermore, exciton dynamics simulations are performed as a way to obtain additional information about the degree of order in OPE columnar phases. Finally, ab initio and molecular mechanics simulations are used to investigate the influence of an applied electric field on pentacene reactivity and stability. The reaction path of pentacene thermal dimerization in the presence of an external electric field is investigated; the results can be related to the fatigue effect observed in OFETs, that show significant performance degradation even in the absence of external agents. In addition to this, the effect of the gate voltage on a pentacene monolayer are simulated, and the results are then compared to X-ray diffraction measurements performed for the first time on operating OFETs.

Concepts and methods for efficient decentralized learning in federated settings

Deep Neural Networks (DNNs) have revolutionized a wide range of applications beyond traditional machine learning and artificial intelligence fields, e.g., computer vision, healthcare, natural language processing and others. At the same time, edge devices have become central in our society, generating an unprecedented amount of data which could be used to train data-hungry models such as DNNs. However, the potentially sensitive or confidential nature of gathered data poses privacy concerns when storing and processing them in centralized locations. To this purpose, decentralized learning decouples model training from the need of directly accessing raw data, by alternating on-device training and periodic communications. The ability of distilling knowledge from decentralized data, however, comes at the cost of facing more challenging learning settings, such as coping with heterogeneous hardware and network connectivity, statistical diversity of data, and ensuring verifiable privacy guarantees. This Thesis proposes an extensive overview of decentralized learning literature, including a novel taxonomy and a detailed description of the most relevant system-level contributions in the related literature for privacy, communication efficiency, data and system heterogeneity, and poisoning defense. Next, this Thesis presents the design of an original solution to tackle communication efficiency and system heterogeneity, and empirically evaluates it on federated settings. For communication efficiency, an original method, specifically designed for Convolutional Neural Networks, is also described and evaluated against the state-of-the-art. Furthermore, this Thesis provides an in-depth review of recently proposed methods to tackle the performance degradation introduced by data heterogeneity, followed by empirical evaluations on challenging data distributions, highlighting strengths and possible weaknesses of the considered solutions. Finally, this Thesis presents a novel perspective on the usage of Knowledge Distillation as a mean for optimizing decentralized learning systems in settings characterized by data heterogeneity or system heterogeneity. Our vision on relevant future research directions close the manuscript.

Computational resources for structural and metagenomic characterization of functions in bacteria and bacterial communities. Antimicrobial resistance, pathways for xenobiotic degradation and relationship between gut microbiome and ageing.

Prokaryotic organisms are one of the most successful forms of life, they are present in all known ecosystems. The deluge diversity of bacteria reflects their ability to colonise every environment. Also, human beings host trillions of microorganisms in their body districts, including skin, mucosae, and gut. This symbiosis is active for all other terrestrial and marine animals, as well as plants. With the term holobiont we refer, with a single word, to the systems including both the host and its symbiotic microbial species. The coevolution of bacteria within their ecological niches reflects the adaptation of both host and guest species, and it is shaped by complex interactions that are pivotal for determining the host state. Nowadays, thanks to the current sequencing technologies, Next Generation Sequencing, we have unprecedented tools for investigating the bacterial life by studying the prokaryotic genome sequences. NGS revolution has been sustained by the advancements in computational performance, in terms of speed, storage capacity, algorithm development and hardware costs decreasing following the Moore’s Law. Bioinformaticians and computational biologists design and implement ad hoc tools able to analyse high-throughput data and extract valuable biological information. Metagenomics requires the integration of life and computational sciences and it is uncovering the deluge diversity of the bacterial world. The present thesis work focuses mainly on the analysis of prokaryotic genomes under different aspects. Being supervised by two groups at the University of Bologna, the Biocomputing group and the group of Microbial Ecology of Health, I investigated three different topics: i) antimicrobial resistance, particularly with respect to missense point mutations involved in the resistant phenotype, ii) bacterial mechanisms involved in xenobiotic degradation via the computational analysis of metagenomic samples, and iii) the variation of the human gut microbiota through ageing, in elderly and longevous individuals.