Ensemble Stream Model for Data-Cleaning in Sensor Networks

Ensemble Stream Modeling and Data-cleaning are sensor information processing systems have different training and testing methods by which their goals are cross-validated. This research examines a mechanism, which seeks to extract novel patterns by generating ensembles from data. The main goal of label-less stream processing is to process the sensed events to eliminate the noises that are uncorrelated, and choose the most likely model without over fitting thus obtaining higher model confidence. Higher quality streams can be realized by combining many short streams into an ensemble which has the desired quality. The framework for the investigation is an existing data mining tool. First, to accommodate feature extraction such as a bush or natural forest-fire event we make an assumption of the burnt area (BA*), sensed ground truth as our target variable obtained from logs. Even though this is an obvious model choice the results are disappointing. The reasons for this are two: One, the histogram of fire activity is highly skewed. Two, the measured sensor parameters are highly correlated. Since using non descriptive features does not yield good results, we resort to temporal features. By doing so we carefully eliminate the averaging effects; the resulting histogram is more satisfactory and conceptual knowledge is learned from sensor streams. Second is the process of feature induction by cross-validating attributes with single or multi-target variables to minimize training error. We use F-measure score, which combines precision and accuracy to determine the false alarm rate of fire events. The multi-target data-cleaning trees use information purity of the target leaf-nodes to learn higher order features. A sensitive variance measure such as f-test is performed during each node’s split to select the best attribute. Ensemble stream model approach proved to improve when using complicated features with a simpler tree classifier. The ensemble framework for data-cleaning and the enhancements to quantify quality of fitness (30% spatial, 10% temporal, and 90% mobility reduction) of sensor led to the formation of streams for sensor-enabled applications. Which further motivates the novelty of stream quality labeling and its importance in solving vast amounts of real-time mobile streams generated today.

Trajectory Privacy Preservation in Mobile Wireless Sensor Networks

In recent years, there has been an enormous growth of location-aware devices, such as GPS embedded cell phones, mobile sensors and radio-frequency identification tags. The age of combining sensing, processing and communication in one device, gives rise to a vast number of applications leading to endless possibilities and a realization of mobile Wireless Sensor Network (mWSN) applications. As computing, sensing and communication become more ubiquitous, trajectory privacy becomes a critical piece of information and an important factor for commercial success. While on the move, sensor nodes continuously transmit data streams of sensed values and spatiotemporal information, known as ``trajectory information". If adversaries can intercept this information, they can monitor the trajectory path and capture the location of the source node. This research stems from the recognition that the wide applicability of mWSNs will remain elusive unless a trajectory privacy preservation mechanism is developed. The outcome seeks to lay a firm foundation in the field of trajectory privacy preservation in mWSNs against external and internal trajectory privacy attacks. First, to prevent external attacks, we particularly investigated a context-based trajectory privacy-aware routing protocol to prevent the eavesdropping attack. Traditional shortest-path oriented routing algorithms give adversaries the possibility to locate the target node in a certain area. We designed the novel privacy-aware routing phase and utilized the trajectory dissimilarity between mobile nodes to mislead adversaries about the location where the message started its journey. Second, to detect internal attacks, we developed a software-based attestation solution to detect compromised nodes. We created the dynamic attestation node chain among neighboring nodes to examine the memory checksum of suspicious nodes. The computation time for memory traversal had been improved compared to the previous work. Finally, we revisited the trust issue in trajectory privacy preservation mechanism designs. We used Bayesian game theory to model and analyze cooperative, selfish and malicious nodes' behaviors in trajectory privacy preservation activities.

Distributed optimization in transportation and logistics networks

Many important problems in communication networks, transportation networks, and logistics networks are solved by the minimization of cost functions. In general, these can be complex optimization problems involving many variables. However, physicists noted that in a network, a node variable (such as the amount of resources of the nodes) is connected to a set of link variables (such as the flow connecting the node), and similarly each link variable is connected to a number of (usually two) node variables. This enables one to break the problem into local components, often arriving at distributive algorithms to solve the problems. Compared with centralized algorithms, distributed algorithms have the advantages of lower computational complexity, and lower communication overhead. Since they have a faster response to local changes of the environment, they are especially useful for networks with evolving conditions. This review will cover message-passing algorithms in applications such as resource allocation, transportation networks, facility location, traffic routing, and stability of power grids.

Méthode de recherche à grand voisinage pour un problème de tournées de véhicules avec flotte privée et transporteur externe

Dans ce mémoire, nous étudions un problème de tournées de véhicules dans lequel une flotte privée de véhicules n’a pas la capacité suffisante pour desservir les demandes des clients. Dans un tel cas, on fait appel à un transporteur externe. Ce dernier n’a aucune contrainte de capacité, mais un coût est encouru lorsqu’un client lui est affecté. Il n’est pas nécessaire de mettre tous les véhicules de la flotte privée en service si cette approche se révèle plus économique. L’objectif consiste à minimiser le coût fixe des véhicules, puis le coût variable de transport et le coût chargé par le transporteur externe. Notre travail consiste à appliquer la métaheuristique de recherche adaptative à grand voisinage sur ce problème. Nous comparons nos résultats avec ceux obtenus précédemment avec différentes techniques connues sur les instances de Christofides et celles de Golden.

Environmental sensing with the Internet of Things

Abstract: After developing many sensor networks using custom protocols to save energy and minimise code complexity - we have now experimented with standards-based designs. These use IPv6 (6LowPAN), RPL routing, Coap for interfaces and data access and protocol buffers for data encapsulation. Deployments in the Cairngorm mountains have shown the capabilities and limitations of the implementations. This seminar will outline the hardware and software we used and discuss the advantages of the more standards-based approach. At the same time we have been progressing with high quality imaging of cultural heritage using the RTIdomes - so some results and designs will be shown as well. So this seminar will cover peat-bogs to museums, binary-HTTP-like REST to 3500 year old documents written on clay.

Educational Intervention Project "R.E.D. Bull(ying)"

Introduction: Adolescence is a stage of life cycle marked by various physical, psychological and social changes. During this stage, young people are faced with the feeling of threat of identity, which may trigger aggressive behaviours. Bullying is a form of school violence with high prevalence, that shouldn't be a "normal" occurrence or a event that young people should experience during the transition between childhood and adolescent. In order to reduce the prevalence of bullying in the school community, we elaborated the Educational Intervention Project "R.E.D. BULL(ying)", with the specific objectives: Evaluate the knowledge level about bullyng, before and after the Project, and increase the level of literacy about the subject in the school community (students and teachers). Methodology: Our target population consisted in a total of 203 students from 5th to 9th grade and 13 teachers of school. It's a cross-sectional study of research - action, with the application of a diagnostic questionnaire, before and after, we conducted the educational sessions. Results: After the educational sessions, 93,1% of students identified what to do in a bullying situation, and 62,6% of students responded that in an assault situation, called an adult; 95,1% said they knew what was bullying, 56,8% associated the concept to physical aggression and 92,6 % mentioned to know the types of bullying, and physical bullying (71,9%) and verbal bullying (69,5%) were the most mentioned types. Meanwhile, the teachers: 76,9% considered that the school environment was pleasant, 84,6% characterized the relationship between the students as "adequate" and 77% said they didn't experience any bullying situation. Conclusions: We found an overall improvement to the level of bullying related knowledge after the educational intervention. So, we verified that the integrated intervention in the school health teams, allows greater attention to the detection, signalling and routing situations of violence.

EDUCATIONAL INTERVENTION PROJECT "R.E.D. BULL(YING)"

Introduction: Adolescence is a stage of life cycle marked by various physical, psychological and social changes. During this stage, young people are faced with the feeling of threat of identity, which may trigger aggressive behaviours. Bullying is a form of school violence with high prevalence, that shouldn't be a "normal" occurrence or a event that young people should experience during the transition between childhood and adolescent. In order to reduce the prevalence of bullying in the school community, we elaborated the Educational Intervention Project "R.E.D. BULL(ying)", with the specific objectives: Evaluate the knowledge level about bullyng, before and after the Project, and increase the level of literacy about the subject in the school community (students and teachers). Methodology: Our target population consisted in a total of 203 students from 5th to 9th grade and 13 teachers of school. It's a cross-sectional study of research - action, with the application of a diagnostic questionnaire, before and after, we conducted the educational sessions. Results: After the educational sessions, 93,1% of students identified what to do in a bullying situation, and 62,6% of students responded that in an assault situation, called an adult; 95,1% said they knew what was bullying, 56,8% associated the concept to physical aggression and 92,6 % mentioned to know the types of bullying, and physical bullying (71,9%) and verbal bullying (69,5%) were the most mentioned types. Meanwhile, the teachers: 76,9% considered that the school environment was pleasant, 84,6% characterized the relationship between the students as "adequate" and 77% said they didn't experience any bullying situation. Conclusions: We found an overall improvement to the level of bullying related knowledge after the educational intervention. So, we verified that the integrated intervention in the school health teams, allows greater attention to the detection, signalling and routing situations of violence.

Distributed Large-scale Mixed-Integer Optimization with Application to Energy and Multi-robot Networks

Several decision and control tasks in cyber-physical networks can be formulated as large- scale optimization problems with coupling constraints. In these "constraint-coupled" problems, each agent is associated to a local decision variable, subject to individual constraints. This thesis explores the use of primal decomposition techniques to develop tailored distributed algorithms for this challenging set-up over graphs. We first develop a distributed scheme for convex problems over random time-varying graphs with non-uniform edge probabilities. The approach is then extended to unknown cost functions estimated online. Subsequently, we consider Mixed-Integer Linear Programs (MILPs), which are of great interest in smart grid control and cooperative robotics. We propose a distributed methodological framework to compute a feasible solution to the original MILP, with guaranteed suboptimality bounds, and extend it to general nonconvex problems. Monte Carlo simulations highlight that the approach represents a substantial breakthrough with respect to the state of the art, thus representing a valuable solution for new toolboxes addressing large-scale MILPs. We then propose a distributed Benders decomposition algorithm for asynchronous unreliable networks. The framework has been then used as starting point to develop distributed methodologies for a microgrid optimal control scenario. We develop an ad-hoc distributed strategy for a stochastic set-up with renewable energy sources, and show a case study with samples generated using Generative Adversarial Networks (GANs). We then introduce a software toolbox named ChoiRbot, based on the novel Robot Operating System 2, and show how it facilitates simulations and experiments in distributed multi-robot scenarios. Finally, we consider a Pickup-and-Delivery Vehicle Routing Problem for which we design a distributed method inspired to the approach of general MILPs, and show the efficacy through simulations and experiments in ChoiRbot with ground and aerial robots.

Development of predictive energy management strategies for hybrid electric vehicles supported by connectivity

Nowadays, the spreading of the air pollution crisis enhanced by greenhouse gases emission is leading to the worsening of the global warming. In this context, the transportation sector plays a vital role, since it is responsible for a large part of carbon dioxide production. In order to address these issues, the present thesis deals with the development of advanced control strategies for the energy efficiency optimization of plug-in hybrid electric vehicles (PHEVs), supported by the prediction of future working conditions of the powertrain. In particular, a Dynamic Programming algorithm has been developed for the combined optimization of vehicle energy and battery thermal management. At this aim, the battery temperature and the battery cooling circuit control signal have been considered as an additional state and control variables, respectively. Moreover, an adaptive equivalent consumption minimization strategy (A-ECMS) has been modified to handle zero-emission zones, where engine propulsion is not allowed. Navigation data represent an essential element in the achievement of these tasks. With this aim, a novel simulation and testing environment has been developed during the PhD research activity, as an effective tool to retrieve routing information from map service providers via vehicle-to-everything connectivity. Comparisons between the developed and the reference strategies are made, as well, in order to assess their impact on the vehicle energy consumption. All the activities presented in this doctoral dissertation have been carried out at the Green Mobility Research Lab} (GMRL), a research center resulting from the partnership between the University of Bologna and FEV Italia s.r.l., which represents the industrial partner of the research project.

Wireless Network Analytics for Next Generation Spectrum Awareness

The importance of networks, in their broad sense, is rapidly and massively growing in modern-day society thanks to unprecedented communication capabilities offered by technology. In this context, the radio spectrum will be a primary resource to be preserved and not wasted. Therefore, the need for intelligent and automatic systems for in-depth spectrum analysis and monitoring will pave the way for a new set of opportunities and potential challenges. This thesis proposes a novel framework for automatic spectrum patrolling and the extraction of wireless network analytics. It aims to enhance the physical layer security of next generation wireless networks through the extraction and the analysis of dedicated analytical features. The framework consists of a spectrum sensing phase, carried out by a patrol composed of numerous radio-frequency (RF) sensing devices, followed by the extraction of a set of wireless network analytics. The methodology developed is blind, allowing spectrum sensing and analytics extraction of a network whose key features (i.e., number of nodes, physical layer signals, medium access protocol (MAC) and routing protocols) are unknown. Because of the wireless medium, over-the-air signals captured by the sensors are mixed; therefore, blind source separation (BSS) and measurement association are used to estimate the number of sources and separate the traffic patterns. After the separation, we put together a set of methodologies for extracting useful features of the wireless network, i.e., its logical topology, the application-level traffic patterns generated by the nodes, and their position. The whole framework is validated on an ad-hoc wireless network accounting for MAC protocol, packet collisions, nodes mobility, the spatial density of sensors, and channel impairments, such as path-loss, shadowing, and noise. The numerical results obtained by extensive and exhaustive simulations show that the proposed framework is consistent and can achieve the required performance.

Automated service provisioning in programmable network infrastructures

Modern networks are undergoing a fast and drastic evolution, with software taking a more predominant role. Virtualization and cloud-like approaches are replacing physical network appliances, reducing the management burden of the operators. Furthermore, networks now expose programmable interfaces for fast and dynamic control over traffic forwarding. This evolution is backed by standard organizations such as ETSI, 3GPP, and IETF. This thesis will describe which are the main trends in this evolution. Then, it will present solutions developed during the three years of Ph.D. to exploit the capabilities these new technologies offer and to study their possible limitations to push further the state-of-the-art. Namely, it will deal with programmable network infrastructure, introducing the concept of Service Function Chaining (SFC) and presenting two possible solutions, one with Openstack and OpenFlow and the other using Segment Routing and IPv6. Then, it will continue with network service provisioning, presenting concepts from Network Function Virtualization (NFV) and Multi-access Edge Computing (MEC). These concepts will be applied to network slicing for mission-critical communications and Industrial IoT (IIoT). Finally, it will deal with network abstraction, with a focus on Intent Based Networking (IBN). To summarize, the thesis will include solutions for data plane programming with evaluation on well-known platforms, performance metrics on virtual resource allocations, novel practical application of network slicing on mission-critical communications, an architectural proposal and its implementation for edge technologies in Industrial IoT scenarios, and a formal definition of intent using a category theory approach.

Analytics and optimization for emergency healthcare processes

This thesis deals with the analysis and management of emergency healthcare processes through the use of advanced analytics and optimization approaches. Emergency processes are among the most complex within healthcare. This is due to their non-elective nature and their high variability. This thesis is divided into two topics. The first one concerns the core of emergency healthcare processes, the emergency department (ED). In the second chapter, we describe the ED that is the case study. This is a real case study with data derived from a large ED located in northern Italy. In the next two chapters, we introduce two tools for supporting ED activities. The first one is a new type of analytics model. Its aim is to overcome the traditional methods of analyzing the activities provided in the ED by means of an algorithm that analyses the ED pathway (organized as event log) as a whole. The second tool is a decision-support system, which integrates a deep neural network for the prediction of patient pathways, and an online simulator to evaluate the evolution of the ED over time. Its purpose is to provide a set of solutions to prevent and solve the problem of the ED overcrowding. The second part of the thesis focuses on the COVID-19 pandemic emergency. In the fifth chapter, we describe a tool that was used by the Bologna local health authority in the first part of the pandemic. Its purpose is to analyze the clinical pathway of a patient and from this automatically assign them a state. Physicians used the state for routing the patients to the correct clinical pathways. The last chapter is dedicated to the description of a MIP model, which was used for the organization of the COVID-19 vaccination campaign in the city of Bologna, Italy.

Ship performance modelling for least-CO2 emission routes

Decarbonization of maritime transport requires immediate action. In the short term, ship weather routing can provide greenhouse gas emission reductions, even for existing ships and without retrofitting them. Weather routing is based on making optimal use of both envi- ronmental information and knowledge about vessel seakeeping and performance. Combining them at a state-of-the-art level and making use of path planning in realistic conditions can be challenging. To address these topics in an open-source framework, this thesis led to the development of a new module called bateau , and to its combination with the ship routing model VISIR. bateau includes both hull geometry and propulsion modelling for various vessel types. It has two objectives: to predict the sustained speed in a seaway and to estimate the CO2 emission rate during the voyage. Various semi-empirical approaches were used in bateau to predict the ship hydro- and aerodynamical resistance in both head and oblique seas. Assuming that the ship sails at a constant engine load, the involuntary speed loss due to waves was estimated. This thesis also attempted to clarify the role played by the actual representation of the sea state. In particular, the influence of the wave steepness parameter was assessed. For dealing with ships with a greater superstructure, the wind added resistance was also estimated. Numerical experiments via bateau were conducted for both a medium and a large-size container ships, a bulk-carrier, and a tanker. The simulations of optimal routes were carried out for a feeder containership during voyages in the North Indian Ocean and in the South China Sea. Least-CO2 routes were compared to the least-distance ones, assessing the relative CO2 savings. Analysis fields from the Copernicus Marine Service were used in the numerical experiments.

Uncovering the encoding and transmission of behaviourally relevant information in neural activity

Most cognitive functions require the encoding and routing of information across distributed networks of brain regions. Information propagation is typically attributed to physical connections existing between brain regions, and contributes to the formation of spatially correlated activity patterns, known as functional connectivity. While structural connectivity provides the anatomical foundation for neural interactions, the exact manner in which it shapes functional connectivity is complex and not yet fully understood. Additionally, traditional measures of directed functional connectivity only capture the overall correlation between neural activity, and provide no insight on the content of transmitted information, limiting their ability in understanding neural computations underlying the distributed processing of behaviorally-relevant variables. In this work, we first study the relationship between structural and functional connectivity in simulated recurrent spiking neural networks with spike timing dependent plasticity. We use established measures of time-lagged correlation and overall information propagation to infer the temporal evolution of synaptic weights, showing that measures of dynamic functional connectivity can be used to reliably reconstruct the evolution of structural properties of the network. Then, we extend current methods of directed causal communication between brain areas, by deriving an information-theoretic measure of Feature-specific Information Transfer (FIT) quantifying the amount, content and direction of information flow. We test FIT on simulated data, showing its key properties and advantages over traditional measures of overall propagated information. We show applications of FIT to several neural datasets obtained with different recording methods (magneto and electro-encephalography, spiking activity, local field potentials) during various cognitive functions, ranging from sensory perception to decision making and motor learning. Overall, these analyses demonstrate the ability of FIT to advance the investigation of communication between brain regions, uncovering the previously unaddressed content of directed information flow.

Analysis and optimisation of books flow among branches through simulation: the case of Trondheim’s Public Library

Il presente lavoro di tesi verte sull’analisi e l’ottimizzazione dei flussi di libri generati tra le diverse sedi della biblioteca pubblica, Trondheim folkebibliotek, situata a Trondheim, città del nord norvegese. La ricerca si inserisce nell’ambito di un progetto pluriennale, SmartLIB, che questa sta intraprendendo con l’università NTNU - Norwegian University of Science and Technology. L’obiettivo di questa tesi è quello di analizzare possibili soluzioni per ottimizzare il flusso di libri generato dagli ordini dei cittadini. Una prima fase di raccolta ed analisi dei dati è servita per avere le informazioni necessarie per procedere nella ricerca. Successivamente è stata analizzata la possibilità di ridurre i flussi andando ad associare ad ogni dipartimento la quantità di copie necessarie per coprire il 90% della domanda, seguendo la distribuzione di Poisson. In seguito, sono state analizzate tre soluzioni per ottimizzare i flussi generati dai libri, il livello di riempimento dei box ed il percorso del camion che giornalmente visita tutte le sedi della libreria. Di supporto per questo secondo studio è stato il Vehicle Routing Problem (VRP). Un modello simulativo è stato creato su Anylogic ed utilizzato per validare le soluzioni proposte. I risultati hanno portato a proporre delle soluzioni per ottimizzare i flussi complessivi, riducendo il delay time di consegna dei libri del 50%, ad una riduzione del 53% del flusso di box e ad una conseguente aumento del 44% del tasso di riempimento di ogni box. Possibili future implementazioni delle soluzioni trovate corrispondono all’installazione di una nuova Sorting Machine nella sede centrale della libreria ed all’implementazione sempre in quest’ultima di un nuovo schedule giornaliero.