Mac protocols for linear wireless (sensor) networks

Wireless sensor networks (WSNs) consist of a large number of sensor nodes, characterized by low power constraint, limited transmission range and limited computational capabilities [1][2].The cost of these devices is constantly decreasing, making it possible to use a large number of sensor devices in a wide array of commercial, environmental, military, and healthcare fields. Some of these applications involve placing the sensors evenly spaced on a straight line for example in roads, bridges, tunnels, water catchments and water pipelines, city drainages, oil and gas pipelines etc., making a special class of these networks which we define as a Linear Wireless Network (LWN). In LWNs, data transmission happens hop by hop from the source to the destination, through a route composed of multiple relays. The peculiarity of the topology of LWNs, motivates the design of specialized protocols, taking advantage of the linearity of such networks, in order to increase reliability, communication efficiency, energy savings, network lifetime and to minimize the end-to-end delay [3]. In this thesis a novel contention based Medium Access Control (MAC) protocol called L-CSMA, specifically devised for LWNs is presented. The basic idea of L-CSMA is to assign different priorities to nodes based on their position along the line. The priority is assigned in terms of sensing duration, whereby nodes closer to the destination are assigned shorter sensing time compared to the rest of the nodes and hence higher priority. This mechanism speeds up the transmission of packets which are already in the path, making transmission flow more efficient. Using NS-3 simulator, the performance of L-CSMA in terms of packets success rate, that is, the percentage of packets that reach destination, and throughput are compared with that of IEEE 802.15.4 MAC protocol, de-facto standard for wireless sensor networks. In general, L-CSMA outperforms the IEEE 802.15.4 MAC protocol.

Studio di Flow-Assurance e sensitivity analysis in sistemi di distribuzione di petrolio e gas naturale

Argomento del lavoro è stato lo studio di problemi legati alla Flow-Asurance. In particolare, si focalizza su due aspetti: i) una valutazione comparativa delle diverse equazioni di stato implementate nel simulatore multifase OLGA, per valutare quella che porta a risultati più conservativi; ii) l’analisi della formazione di idrati, all’interno di sistemi caratterizzati dalla presenza di gas ed acqua. Il primo argomento di studio nasce dal fatto che per garantire continuità del flusso è necessario conoscere il comportamento volumetrico del fluido all’interno delle pipelines. Per effettuare tali studi, la Flow-Assurance si basa sulle Equazioni di Stato cubiche. In particolare, sono state confrontate: -L’equazione di Soave-Redlich-Kwong; -L’equazione di Peng-Robinson; -L’equazione di Peng-Robinson modificata da Peneloux. Sono stati analizzati 4 fluidi idrocarburici (2 multifase, un olio e un gas) con diverse composizioni e diverse condizioni di fase. Le variabili considerate sono state pressione, temperatura, densità e viscosità; sono state poi valutate le perdite di carico, parametro fondamentale nello studio del trasporto di un fluido, valutando che l'equazione di Peng-Robinson è quella più adatta per caratterizzare termodinamicamente il fluido durante una fase di design, in quanto fornisce l'andamento più conservativo. Dopo aver accertato la presenza di idrati nei fluidi multifase, l’obiettivo del lavoro è stato analizzare come il sistema rispondesse all’aggiunta di inibitori chimici per uscire dalla regione termodinamica di stabilità dell’idrato. Gli inibitori utilizzati sono stati metanolo e mono-etilen-glicole in soluzione acquosa. L’analisi è stata effettuata confrontando due metodi: -Metodo analitico di Hammerschmidt; -Metodo iterativo con PVTSim. I risultati ottenuti hanno dimostrato che entrambi gli inibitori utilizzati risolvono il problema della formazione di idrato spostando la curva di stabilità al di fuori delle pressioni e temperature che si incontrano nella pipeline. Valutando le quantità da iniettare, il metodo di Hammerschmidt risulta quello più conservativo, indicando portate maggiori rispetto al PVTsim, soprattutto aggiungendo metanolo.

Models for the assessment of resilience in water distribution networks

The following thesis work focuses on the use and implementation of advanced models for measuring the resilience of water distribution networks. In particular, the functions implemented in GRA Tool, a software developed by the University of Exeter (UK), and the functions of the Toolkit of Epanet 2.2 were investigated. The study of the resilience and failure, obtained through GRA Tool and the development of the methodology based on the combined use of EPANET 2.2 and MATLAB software, was tested in a first phase, on a small-sized literature water distribution network, so that the variability of the results could be perceived more clearly and with greater immediacy, and then, on a more complex network, that of Modena. In the specific, it has been decided to go to recreate a mode of failure deferred in time, one proposed by the software GRA Tool, that is failure to the pipes, to make a comparison between the two methodologies. The analysis of hydraulic efficiency was conducted using a synthetic and global network performance index, i.e., Resilience index, introduced by Todini in the years 2000-2016. In fact, this index, being one of the parameters with which to evaluate the overall state of "hydraulic well-being" of a network, has the advantage of being able to act as a criterion for selecting any improvements to be made on the network itself. Furthermore, during these analyzes, was shown the analytical development undergone over time by the formula of the Resilience Index. The final intent of this thesis work was to understand by what means to improve the resilience of the system in question, as the introduction of the scenario linked to the rupture of the pipelines was designed to be able to identify the most problematic branches, i.e., those that in the event of a failure it would entail greater damage to the network, including lowering the Resilience Index.

A Distributed Support based on Kubeflow to Ease MLOps of Federated Learning Algorithms

The main objective of my thesis work is to exploit the Google native and open-source platform Kubeflow, specifically using Kubeflow pipelines, to execute a Federated Learning scalable ML process in a 5G-like and simplified test architecture hosting a Kubernetes cluster and apply the largely adopted FedAVG algorithm and FedProx its optimization empowered by the ML platform ‘s abilities to ease the development and production cycle of this specific FL process. FL algorithms are more are and more promising and adopted both in Cloud application development and 5G communication enhancement through data coming from the monitoring of the underlying telco infrastructure and execution of training and data aggregation at edge nodes to optimize the global model of the algorithm ( that could be used for example for resource provisioning to reach an agreed QoS for the underlying network slice) and after a study and a research over the available papers and scientific articles related to FL with the help of the CTTC that suggests me to study and use Kubeflow to bear the algorithm we found out that this approach for the whole FL cycle deployment was not documented and may be interesting to investigate more in depth. This study may lead to prove the efficiency of the Kubeflow platform itself for this need of development of new FL algorithms that will support new Applications and especially test the FedAVG algorithm performances in a simulated client to cloud communication using a MNIST dataset for FL as benchmark.

A framework for the environmental risk assessment of offshore carbon storage in CCS systems

Carbon capture and storage (CCS) represents an interesting climate mitigation option, however, as for any other human activity, there is the impelling need to assess and manage the associated risks. This study specifically addresses the marine environmental risk posed by CO2 leakages associated to CCS subsea engineering system, meant as offshore pipelines and injection / plugged and abandoned wells. The aim of this thesis work is to start approaching the development of a complete and standardized practical procedure to perform a quantified environmental risk assessment for CCS, with reference to the specific activities mentioned above. Such an effort would be of extreme relevance not only for companies willing to implement CCS, as a methodological guidance, but also, by uniformizing the ERA procedure, to begin changing people’s perception about CCS, that happens to be often discredited due to the evident lack of comprehensive and systematic methods to assess the impacts on the marine environment. The backbone structure of the framework developed consists on the integration of ERA’s main steps and those belonging to the quantified risk assessment (QRA), in the aim of quantitatively characterizing risk and describing it as a combination of magnitude of the consequences and their frequency. The framework developed by this work is, however, at a high level, as not every single aspect has been dealt with in the required detail. Thus, several alternative options are presented to be considered for use depending on the situation. Further specific studies should address their accuracy and efficiency and solve the knowledge gaps emerged, in order to establish and validate a final and complete procedure. Regardless of the knowledge gaps and uncertainties, that surely need to be addressed, this preliminary framework already finds some relevance in on field applications, as a non-stringent guidance to perform CCS ERA, and it constitutes the foundation of the final framework.

Experimental characterization of hydrogen containing flames

The current environmental and socio-economic situation promotes the development of carbon-neutral and sustainable solutions for energy supply. In this framework, the use of hydrogen has been largely indicated as a promising alternative. However, safety aspects are of concern for storage and transportation technologies. Indeed, the current know-how promotes its transportation via pipeline as compressed gas. However, the peculiar properties of hydrogen make the selection of suitable materials challenging. For these reasons, dilution with less reactive species has been considered a short and medium solution. As a way of example, methane-hydrogen mixtures are currently transported via pipelines. In this case, the hydrogen content is limited to 20% in volume, thus keeping the dependence on natural gas sources. On the contrary, hydrogen can be conveniently transported by mixing it with carbon dioxide deriving from carbon capture and storage technologies. In this sense, the interactions between hydrogen and carbon dioxide have been poorly studied. In particular, the effects of composition and operative conditions in the case of accidental release or for direct use in the energy supply chain are unknown. For these reasons, the present work was devoted to the characterization of the chemical phenomena ruling the system. To this aim, laminar flames containing hydrogen and carbon dioxide in the air were investigated experimentally and numerically. Different detailed kinetic mechanisms largely validated were considered at this stage. Significant discrepancies were observed among numerical and experimental data, especially once a fuel consisting of 40%v of hydrogen was studied. This deviation was attributed to the formation of a cellular flame increasing the overall reactivity. Hence, this observation suggests the need for combined models accounting for peculiar physical phenomena and detailed kinetic mechanisms characterizing the hydrogen-containing flames.

Continual learning on nano drones

Miniaturized flying robotic platforms, called nano-drones, have the potential to revolutionize the autonomous robots industry sector thanks to their very small form factor. The nano-drones’ limited payload only allows for a sub-100mW microcontroller unit for the on-board computations. Therefore, traditional computer vision and control algorithms are too computationally expensive to be executed on board these palm-sized robots, and we are forced to rely on artificial intelligence to trade off accuracy in favor of lightweight pipelines for autonomous tasks. However, relying on deep learning exposes us to the problem of generalization since the deployment scenario of a convolutional neural network (CNN) is often composed by different visual cues and different features from those learned during training, leading to poor inference performances. Our objective is to develop and deploy and adaptation algorithm, based on the concept of latent replays, that would allow us to fine-tune a CNN to work in new and diverse deployment scenarios. To do so we start from an existing model for visual human pose estimation, called PULPFrontnet, which is used to identify the pose of a human subject in space through its 4 output variables, and we present the design of our novel adaptation algorithm, which features automatic data gathering and labeling and on-device deployment. We therefore showcase the ability of our algorithm to adapt PULP-Frontnet to new deployment scenarios, improving the R2 scores of the four network outputs, with respect to an unknown environment, from approximately [−0.2, 0.4, 0.0,−0.7] to [0.25, 0.45, 0.2, 0.1]. Finally we demonstrate how it is possible to fine-tune our neural network in real time (i.e., under 76 seconds), using the target parallel ultra-low power GAP 8 System-on-Chip on board the nano-drone, and we show how all adaptation operations can take place using less than 2mWh of energy, a small fraction of the available battery power.