PhD

Urban water networks serve as lifelines for densely populated areas, ensuring access to clean water for drinking, sanitation, and industrial purposes. These infrastructures form the backbone of urban life. However, in recent years, water science has faced urgent challenges, including the need for wastewater surveillance to detect viruses and the optimization of reclaimed water networks to address water scarcity exacerbated by climate change. This thesis applies graph theory to address these challenges in urban water networks. Graph theory, which studies mathematical structures composed of nodes and edges, offers innovative strategies for enhancing network surveillance, design, and resilience. Urban water distribution networks are modeled as undirected graphs, while wastewater networks are represented as directed graphs. The research employs a five-phase methodology: literature review, data acquisition, data preparation, algorithm development, and data analysis. This process automates data gathering and processing, resulting in significant efficiencies. Girona and Lloret de Mar cities serve as case studies for testing and validating the developed algorithms. One key achievement is the development of a sewage monitoring site selection algorithm, which optimally balances coverage and interference considerations. This algorithm has proven highly beneficial for pandemic management, aiding in the early detection of COVID-19 in wastewater. After sewage network monitoring, efforts focused on improving network resilience, starting with analyzing tree root impacts on wastewater networks. A tree rearrangement algorithm was created to mitigate pipe failure risks in wastewater networks, yielding significant cost savings despite initial investments. In reclaimed water distribution networks (WDNs), two novel proposals are presented for designing resilient and cost-effective systems. These proposals compute optimal network designs, delivering reclaimed water up to three times more efficiently than manual planning. The algorithms prioritize resilience and cost savings, leading to substantial water conservation, which is crucial in drought conditions. These solutions are integrated into the REWATnet tool and repository. This doctoral thesis significantly contributes by integrating computer science, graph theory, and water sciences. It addresses pressing issues from the COVID-19 pandemic to environmental preservation and water scarcity. The innovative algorithms and tools developed enhance the efficiency, accessibility, and sustainability of urban water systems

This thesis addresses key challenges in the optimization of network slicing in Beyond 5G (B5G) networks, focusing on the use of Graph Neural Networks (GNNs) for performance prediction and resource allocation. It is structured into three main parts: improvement of an existing GNN model for Key Performance Indicator (KPI) prediction, dataset creation for network slicing, and the creation of a GNN model for predicting network slicing KPIs. The ultimate goal of this work is to build a model for predicting network slicing KPIs. GNNs models are a novel and powerful technique for accurately learning from graph-structured data, making them suitable for predicting network KPIs. To learn GNNs programming, the first part of this work describes the participation in a ITU challenge. Autonomous network management is explored, being essential for the dynamic environments expected in B5G networks. The limitations of traditional modeling tools and network simulators are also explored, proposing GNNs as an effective alternative due to their high accuracy and low computational requirements. A significant contribution is the enhancement of the RouteNet baseline model, achieving an improvement in prediction accuracy for larger networks, in comparison to the networks seen during training. As the goal is to build a GNNs model for predicting network slicing KPIs, and a lack of data containing network slicing scenarios is identified, the second part presents a the creation of a network slicing dataset designed to support Artificial Intelligence (AI)-based performance prediction in B5G networks. This dataset, generated through a packet-level simulator, includes diverse network scenarios with varying topologies, slice instances, and traffic flows, capturing the complexities of Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and Massive Internet of Things (mIoT) slices. The dataset is a valuable resource for the research community, facilitating innovations in network slicing and resource management. After creating the required data, the GNN model called GNNetSlice is developed in part three, introducing a novel model that leverages GNNs to predict the performance of network slices in the core and transport network. By adopting a data-driven approach, GNNetSlice balances prediction speed and accuracy. The model demonstrates high accuracy in predicting delay, jitter, and losses across various scenarios. Overall, this thesis makes contributions to the field of network slicing, providing tools and datasets for efficient and accurate KPI prediction in B5G networks. The proposed models and datasets pave the way for more resilient and adaptive network management solutions, crucial for the next generation of mobile networks

The development of any process handles data. These data are generated from information that in turn generates knowledge. The ideal is that this knowledge provides the process with added value that allows it to improve, take advantage of what it has and reuse it in future processes framed of what has been learned. Knowledge can be generated with the development of activities and dynamics, extracting it from documents or emails and from the knowledge that people have. In the case of this research, Knowledge Management allows the creation of educational material, in the context of High Abilities students, and whose purpose is to support and encourage co-creation participants, especially these students, co-creating an educational material according to their characteristics. The co-creation of educational material involves processes that are repeated and in which, to facilitate its development, it is important to guide, communicate, interact and collaborate among the participants. In this case, the participants are High Abilities students, their teachers and their parents. Therefore, it is important not only to consider what is related to the process, but also the knowledge that both teachers and parents have about students and their academic process, along with the knowledge the process has about students, such as their personal characteristics. High Abilities Students are those who have different abilities at a higher level than the average for people of their age, to perform in different fields. Despite this advantage, they may have various problems such as difficulty relating socially, lack of understanding for their way of thinking or not being taken into account in the development of different activities of their interest, which leads to demotivation about their educational process in general. From this, the objective is that Knowledge Management in a co-creation process helps to minimize the consequences of these problems, thus improving the motivation of these students in their academic process. The identified problem is that there is no technological framework that allows Knowledge Management for co-creation processes with High Abilities students and that also uses the implicit knowledge that the participants in the process have (students, teachers and parents) and the characteristics of the students

Generally, High Ability or Gifted individuals show spontaneous and natural performance in one or more domains. Thus, a High Ability student is creative, 2/2 curious and motivated to learn new things. Despite this behaviour, these students may lose interest in their learning process especially when their physical and emotional development is different from their intellectual development (desynchronises). The main objective of this thesis is to help motivate High Ability students into their learning process make them active participants in the creation of their learning material. To achieve this we work three different areas together: High Abilities, Co Creation and Adaptation. With this union we designed a learning material co creation process adapted to High Ability student’s characteristics. In this process the student, his/her family and teacher jointly participate in the learning material co creation, which can be applied in his/her learning process

Heritage Education is the process that enables people to understand and appropriate their tangible and intangible heritage. Traditionally, Heritage Education takes place in educational environments (within the school), where the teacher transmits the necessary knowledge to the students. This traditional approach has lost interest among students, and young people are becoming more apathetic and less interested in the cultural and material heritage of their territory. Bearing this in mind, it is necessary to design learning experiences to facilitate the development of Heritage Education processes and to motivate people to interact with their heritage. An alternative for the design of this type of learning experiences is the use of Augmented Reality in the context of Heritage Education, taking into account that Augmented Reality overlays digital information in the real world, facilitating real interaction between users and heritage.

This Thesis focuses on the problem of generic routing in Cayley Graphs(CGs). These graphs are a geometric representation of algebraic groups and have been used as topologies of a wide variety of communication networks. The problem is analyzed from the Automatic Group Theory (AGT), which states that the structure of CGs can be encoded in a set of automatons. From these approach, word-processing techniques are used to design a generic routing scheme that has low complexity; guarantees packet delivery; and provides minimal routing, path diversity and fault-tolerance. These scheme is supported on a set low complexity algorithms for path computation in CGs. The contributions of this Thesis also include an analysis of the topological properties of CGs and their impact on the performance and robustness of networks that use them as topology

In this thesis, the Adaptative Hypermedia Systems (AHS) are used to generate e-Learning processes that consider the characteristics of university students who suffer from Attention Deficit Hyperactivity Disorder (ADHD). Overall, it was proposed a solution that ranges from symptoms detection to academic intervention. Specifically, it was developed a student model based on personal, demographic, academic, behavioral conduct, background and cognitive performance information to create personal student profiles, which indicate if an e-Learning student could have ADHD symptoms. Afther that, considering preferences and strengths of university students suffering from ADHD, three didactic strategies were integrated in the academic environment with the aim of obtaining a better e-Learning experience and academic performance, these strategies are: a serious game, a reusable learning object based on gamification and the use of Universal Design for Learning (UDL).

Most of transportation networks interacts with other to support our modern society way of life. The proper performance of interdependent networks depends on the normal operation of the networks that are interconnected. The aim of this thesis is to measure and analyze the robustness of different interdependent networks models under large-scale failures and, in particular, to consider interdependent networks where at least one of the networks is a telecommunication network. Both, the effects of different network models and the dynamic process of failure propagation between networks are considered. New interconnection strategies are proposed to improve the robustness of the interconnected networks by analyzing the vulnerability of networks to failures and targeted attacks. Moreover, an enhanced region-based interconnection model is proposed by considering a limit to the number of interlinks between the interconnected nodes.

The adoption of Open Educational Resources (OER) has been continuously growing and with it the need to addressing the diversity of students’ learning needs. Because of that, OER should meet with characteristics such as the web accessibility and quality. Thus, teachers as the creators of OER need supporting tools and specialized competences. The main contribution of this thesis is a Learning Analytics Model to Trace the Creation and Evaluation of OER (LAMTCE) considering web accessibility and quality. LAMTCE also includes a user model of the teacher’s competences in the creation and evaluation of OER. Besides that, we developed ATCE, a learning analytics tool based on the LAMTCE model. Finally, it was carried out an evaluation conducted with teachers involving the use of the tool and we found that the tool really benefited teachers in the acquisition of their competences in creation and evaluation of accessible and quality OER.

SDN is a network technology that separates control functions and the data plane. This separation allows flexibility in the management and use of network resources because the software is specialized in controlling the traffic and economic hardware oversees forwarding. Developers can build applications that control the detail of network and packet processing, from the autonomous configuration to complex operations which involve the context. However, the human factor represents between 50% and 80% of network failures due to errors and bugs in the programming of applications and the implementation of algorithms and protocols. This doctoral thesis proposes 1) to use formal specification and verification of network functionalities to reduce the impact of network failures. 2) It presents the guide of network administration through policy implementation for security and auditing, and 3) shows the impact of failures on a representation of SDN architecture as an interdependent network model.

In this thesis, Augmented Reality Game-Based Learning (ARGBL) is explored and arguments are given in favor of its application in the classrooms. The thesis explores the concept and proposes technologies, theories and recommendations to help teachers and designers to include it in their learning activities. Here, the thesis shows a state of art on AR and GBL showing the previous works that support its application. This state of art also shows the strategies that have been used to design and create AR and GBL experiences An exploratory scenario is shown where an ARGBL game was used for a reading comprehension activity in a classroom; an AR book involving a game was used. The thesis proposes a method named “Co-CreARGBL” that is meant to guide teachers and professional designers in the creation, deployment and evaluation of ARGBL experiences. Finally. the thesis argues for the validity of the method

One of the advantages of Augmented Reality (AR) in education is that AR increases student motivation. However, there is a lack of research on guidelines to inform the design and development of motivational AR learning experiences. In this doctoral thesis, we introduce a framework to inform the design and development of motivational AR learning experiences in the Vocational Education and Training (VET) level of education. The research process started with two exploratory studies that were conducted in the VET programme of car’s maintenance in which the Paint-cAR application was co-created with teachers. Then, a study on predictors of student motivation was conducted and framework was subsequently defined. The framework was validated in the VET programme of Laboratory Operations in the field of chemistry and we found that the learning experience positively impact the four dimensions of the ARCS (Attention, Relevance, Confidence and Satisfaction) model of motivation

Network science has significantly advanced in the last decade, providing insights into the underlying structure and dynamics of complex networks. Critical infrastructures such as telecommunication networks play a pivotal role in ensuring the smooth functioning of modern day living. These networks have to constantly deal with failures of their components. In multiple failure scenarios, where traditional protection and restoration schemes are not suitable because of the quantity of resources that would be required, the concept of robustness is used in order to quantify just how good a network is under such a large-scale failure scenario. The aim of this thesis is to, firstly, investigate the current challenges that might lead to multiple failure scenarios of present day networks and, secondly, to propose novel metrics able to quantify the network robustness.

This research work explores the use of the Greedy Geometric Routing (GGR) schemes to solve the scalability problem of the routing systems in Internet-like networks and several families of Data Center architectures. We propose a novel and simple embedding of any connected finite graph into a Word-Metric space, i.e., a metric space generated by algebraic groups. Then, built on top of this greedy embedding, we propose three GGR schemes and we prove the theoretical upper bounds of the Routing Table size, vertex label size and stretch. The first scheme works for any kind of graph and the other two are specialized for Internet-like and several families of DC topologies

During the past years, the adoption of Learning Management System (LMS) to support an e-learning process has been continuously growing. Hence, a potential need and meaningful factor to provide a personalized support, within the context of these systems, has been the identification of particular characteristics of students to provide adaptations of the system’s elements to the individual traits. One particular characteristic that has been little studied in a personalized e-learning process are the learning disabilities (LD) of students. Dyslexia is a common LD in Spanish-speaking university students, which is specifically referred to the manifestation of different difficulties in reading. Dyslexia requires special attention by higher educational institutions to detect, assess, and assist affected students during their learning process. Thereby, an open challenge has been identified from this implication: How to include Spanish-speaking university students with dyslexia and/or reading difficulties in an e-learning process?

A trace of lifelong-learning qualifications has become more mandatory at the European and even at world level. However, for higher education courses, the former could imply complex learning designs and abundance of data to monitor, analyze, and report. This work combine the ideas of personalized, competence-based, and social learning by providing course lifecycle support through competence-based design, outcome based assessment, social learning context analytics, and open student modeling visualizations. A series of studies using a virtual learning environment exploited the idea of the approach and revealed promising results. These results demonstrated the approach helped students and teachers to trace learning outcomes of the European Qualifications Framework (EQF) in higher education courses. Thus, this thesis extends the approach of higher education to a larger collection of learning objects for designing, assessing, and analyzing courses. Moreover, this approach verifies its capability of supporting social context visualization for online and blended personalized education.

Mobile learning (m-learning) is still in its infancy, and great efforts should be made so as to investigate the potentials of an educational paradigm shift from the traditional one-size-fits-all teaching approaches to an adaptive learning that can be delivered via mobile devices. Thus, the next challenge has been identified from this implication: How learning design can be implemented so as to benefit from the m-learning characteristics and achieve adaptation and personalization of the individual learning process in different contexts? An important factor for achieving personalized and adaptive m-learning has been the pedagogically meaningful and technically feasible processing of learners’ contextual information. Therefore in this work, design and delivery of personalized educational scenarios are suggested to be re-thought so as to benefit from the affordances of mobile technologies and the learners’ context

The research we have conducted has been aimed at developing a new mechanism of extraction and use of parallelism at a high level, based on speculation techniques, in cluster environments. This allows our mechanism to be architecture independent and to be adapted to heterogeneous environments. As a drawback there are some limitations due to the fact of working at a high level, such as memory address access dependencies and those occurring in vector operations. The Master/Slave Speculative Parallelization Architecture for Computer Clusters (MSSPACC) is considered that works at the TLP taking advantage of the execution tools of the ILP level. It works at TLP level because the program is divided into threads which we call blocks. The blocks they are considered like single instructions and follow the same pattern of a superscalar processor ILP (input variables are considered like operands and the output like the result of the execution).

Cognitive Radio (CR) is one possible option for mitigating the inefficient wireless spectrum distribution that occurs as a result of fixed spectrum allocation. The use of Dynamic Spectrum Access capabilities will potentially enable secondary users to utilize available and unoccupied frequency slots (channels) whenever the licensed users for those channels are absent. In Cognitive Radio Networks (CRNs), whenever users access the spectrum in an opportunistic manner, control messaging is a crucial issue to ensure that secondary users, i.e. Cognitive Radio Users (CRUs), do not interfere with the licensed users, i.e. Primary Users. In CRNs, where not all CRUs share the same set of channels, i.e. CRUs with Heterogeneous Frequency Devices (HFD), a set of channels must be chosen with care to allow all CRUs in the network to be able to transmit and receive control information. The thesis considers how Control Messaging Schemes (CMSs) can be used within CRNs and proposes a novel CMS for a CRN supporting HFDs. The thesis starts by classifying the CMSs; generating a new taxonomy and identifying the main characteristics for an efficient CRN with HFD. Then, different mathematical approaches for choosing the set of channels used for control information are presented. Next, a CMS for a CRN with HFDs model based upon the aforementioned characteristics and calculating the minimum number of channels for transmitting control information is proposed. Finally the thesis concludes with a number of CMS being presented and evaluated in terms of their impact upon transmission efficiency.

This thesis aim for promoting the learning objects economy by offering teacher the possibility of generating adaptive and standardized learning designs. The adaptation of the generated learning design considers two of the most relevant users characteristic: their competences and their learning styles. Standardized and Adaptive Learning Design Generation Process was implemented using HTN planning. Generation process considers a few inputs from the teachers, in particular, those related with the standardized competence definition, the learning objects metadata as well as the data from the initial student model used with adaptation purposes. The learning designs generation process was enriched through the design of two processes, the learning objects searching and positioning processes. These processes permit to look for learning objects in distributed learning objects repositories and use them as inputs for a generated learning design. A layered evaluation process was developed in order to validate the solutions.

This thesis studies robustness against large-scale failures in communications networks. If failures are isolated, they usually go unnoticed by users thanks to recovery mechanisms. However, such mechanisms are not effective against large-scale multiple failures. Large-scale failures may cause huge economic loss. A key requirement towards devising mechanisms to lessen their impact is the ability to evaluate network robustness. This thesis focuses on multilayer networks featuring separated control and data planes. The majority of the existing measures of robustness are unable to capture the true service degradation in such a setting, because they rely on purely topological features. One of the major contributions of this thesis is a new measure of functional robustness. The failure dynamics is modeled from the perspective of epidemic spreading, for which a new epidemic model is proposed. Another contribution is a taxonomy of multiple, large-scale failures, adapted to the needs and usage of the field of networking.

Ethernet is starting to move from Local area networks to carrier networks. Nevertheless as the requirements of carrier networks are more demanding, the technology needs to be enhanced. Schemes designed for improving Ethernet to match carrier requirements can be categorized in two classes. The first class improves Ethernet control components only (STP based technologies), and the second class improves both Ethernet control and forwarding components (label based forwarding technologies). This thesis analyzes and compares label space usage for the label based forwarding technologies to ensure their scalability. The applicability of existing techniques and studies that can be used to overcome or reduce label scalability issues is evaluated. Additionally this thesis proposes an ILP to calculate optimal performance of STP based approaches and compares them with label based forwarding technologies to be able to determine, given a specific scenario, which approach to use.

The aim of this thesis is to improve the effectiveness and efficiency of virtual learning environments. To that end, it defines a user model that considers at the same time the characteristics of the user, the context and the interaction. These three dimensions are embodied into an Integrated user model (MUI, from the Spanish Modelo de Usuario Integral) to provide adaptation of content, format and educational activities in heterogeneous environments of users, technologies and interactions. One problem that arises in such heterogeneous environments is that inadequate content, format and activities are delivered to students. The MUI is a general model, and as an example of its application, an Integrate Learner Model (MEI, from the Spanish Modelo de Estudiante Integral) is introduced in an educational setting. The main contributions of this thesis are the definition and validation of a MUI, the use of an open MEI to promote student reflection on their learning experience, technology integration with platform independence and validation of the MEI with students in the real world.

In this thesis we propose two network schemes with admission control for TCP elastic traffic using simple mechanisms. Both schemes are able to provide different throughputs and isolation between flows, where a flow is defined as a sequence of related packets within a TCP connection. Regarding to the architecture, both use packet classes with different discarding priorities and an admission control that is implicit, edge-to-edge and based on measurements. In the first scheme, measurements are per-flow, while in the second one, measurements are per-aggregate. The first scheme achieves a good performance using a special modification of TCP sources, while the second scheme achieves a good performance with standard TCP sources. Both schemes have been evauated succesfully through simulation in different network topologies and traffic loads.

All-Optical Label Swapping (AOLS) forms a key technology towards the implementation of All-Optical Packet Switching nodes for the future optical Internet. However, the capital expenditures of the deployment of AOLS increases with the size of the label spaces. Since AOLS working principle is a particular case of the MultiProtocol Label Switching (MPLS) protocol, this thesis studies generic methods, applicable to both, in order to reduce as much as possible the label space. ILP models and heuristics are proposed for the case in which it is allowed to stack one extra label. In general, we found that 50% of the label space can be saved, if it is permitted to push one extra label in the stack. For the case of AOLS, we found that we can reduce the label space down to 25% if we are allowed to double the link capacity and reroute the traffic.

Many new multicast applications emerging from the Internet, such as TV over the Internet, Radio over the Internet, Video Streaming multi-point, etc., need the following resource requirements: bandwidth consumption, end-to-end delay, packet loss ratio, etc. It is therefore necessary to formulate a proposal to specify and provide for these kinds of applications the resources necessary for them to function well.

In this thesis, we propose a multi-objective traffic engineering scheme using different distribution trees to multicast several flows. In this case, we are using the multipath approach to every egress node to obtain the multitree approach and of this way to create several multicast tree. Moreover, our proposal solves the traffic split ratio for multiple trees. The proposed approach can be applied in Multiprotocol Label Switching (MPLS) networks by allowing explicit routes in multicast events to be established.

In the first instance, the aim is to combine the following weighting objectives into a single aggregated metric: the maximum link utilization, the hop count, the total bandwidth consumption, and the total end-to-end delay. We have formulated this multi-objective function (MHDB-S model) and the results obtained using a solver show that several weighting objectives are decreased and the maximum link utilization is minimized.

The problem is NP-hard, therefore, an algorithm is proposed for optimizing the different objectives. The behavior we get using this algorithm is similar to what we get with the solver.

Normally, during multicast transmission the egress node can leave and enter of the tree and for this reason in this thesis we propose a multi-objective traffic engineering scheme using different distribution trees for dynamic multicast groups (i.e. in which egress nodes can change during the connection's lifetime). If a multicast tree is recomputed from scratch, it may consume a considerable amount of CPU time and all communication using the multicast tree will be temporarily interrupted. To alleviate these drawbacks we propose an optimization model (dynamic model MHDB-D) that uses a previously computed multicast tree (static model MHDB-S) adding new egress nodes.

Using the weighted sum method to solve the analytical model is not necessarily correct, because is possible to have a non-convex space solution and some solutions cannot be found. In addition, other kinds of objectives were found in different research works. For the above reasons, a new model called GMM is proposed and to find a solution to this problem a new algorithm using a Multi-Objective Evolutionary Algorithm (MOEA) is proposed too. This algorithm is inspired by the Strength Pareto Evolutionary Algorithm (SPEA).

To give a solution to the dynamic case with this generalized model a dynamic GMM model is proposed and a computational solution using Breadth First Search (BFS) probabilistic is also proposed to give a solution to the dynamic case in multicast.

Finally, in order to evaluate our proposed optimization scheme, we performed the necessary simulations and tests.

The main contributions of this thesis are the taxonomy, the optimization model and the formulation of the multi-objective function in static and dynamic multicast transmission (MHDB-S and MHDB-D), as well as the different algorithms proposed to give computational solutions to this problem. Finally, the generalized model with several functions found in different research works in static and dynamic multicast transmission (GMM and Dynamic GMM), as well as the different algorithms proposed to give computational solutions using MOEA and BFS probabilistic.

This dissertation focuses on the problem of providing mechanisms for routing point to point and multipoint connections in ATM networks. In general the notion of multipoint connection refers to connections that involve a group of users with more than two members. The main objective of this dissertation is to contribute to design efficient routing protocols with alterative routes in fully connected VP-based ATM Networks for call establishment of point to point and multipoint VC connections. An efficient route should be computed during this connection establishment phase.