The thesis focuses on investigating the role of IP Multimedia Subsystem (IMS) in 5G networks. IMS already plays a very important role in enabling a wide range of real-time multimedia communication services such as basic phone calls and messaging in the LTE network. Addition of application servers on top of the IMS core can provide enhanced functionalities like presence, advanced messaging and SIP trunking. IMS guarantees quality, security and reliability of multimedia services when serving users without the installation of any application as well as flexibility over access. This sets IMS apart from other third party applications found on the internet. IMS was created with the idea of being adaptable to the evolving technology. With the implementation of the 5G network underway, a study of the impact of this new architecture on the IMS services is imminent. The thesis focuses on the study of different network elements participating in an IMS service as well as investigating IMS voice and video calls over the 5G network. The thesis consists of two parts: The first part involves exploring the role of IMS in 5G networks. A brief overview of the evolution of mobile networks will help understand the differences between 1G/2G/3G/4G. Following this, IMS and its role in enabling multimedia services in the LTE network is explored. Next, the 5G System Architecture is explained along with components and their functions. Next, a comparison between the elements in 4G and 5G provides a clear understanding of the technological evolution and the procedure involved. The next steps would include understanding the IMS call flow in LTE networks. This would provide a good foundation to understand how the IMS services will be provided over the 5G network. The second part of the thesis includes testing the voice and video services over Ericsson’s 5G network at their Rijen office. As a starting step, the voice and video services are tested using WebRTC. Upon succeeding in the peer-to-peer test, the next step is establishing connectivity to Ericsson’s IMS network at Kista, Sweden. This will allow the testing of IMS voice and video services over the 5G network. The results are recorded and analysed. These are in agreement with the theoretical expectations.

The demand for computational resources is increasing exponentially due to an increasing amount of digital services. Cloud computing is becoming the standard for enterprises to provide these resources. This resulted in hyperscalers which consist of a large number of servers. Data centers consume more than 1% of the world’s electrical energy. Therefore, many techniques are developed that both help to fulfil the needs of digital services and reduce the energy consumption of data center services. Modern-day servers can switch between different operating states which are often integrated in a power configuration mode of servers known as eco-mode. One of these techniques throttles the clock frequency of a central processing unit (CPU) which enables the possibility to lower the power needed for that CPU. This technique is known as dynamic frequency and voltage scaling (DFVS) and the states it switches between are known as performance states (P-states). A different technique that is integrated with eco-mode is the ability to switch between different idle states of the CPU. These states define whether certain caches of the CPU are flushed or not to conserve energy. These states are known as core states (C-states). A different approach that is focused on conserving energy is virtualisation within data centers. Virtualisation enables one physical server to host multiple virtual instances of servers (virtual machines). This reduces resource wastage and energy consumption of a data center. However, this creates the need for a strategic placement that ensures that the demands of the virtual machines are met and that minimises energy consumption and resource wastage. This thesis analyses four of these techniques: the best fit decreasing (BFD) algorithm, the integer linear programming (ILP) algorithm, the particle swarm optimisation (PSO) algorithm and the genetic algorithm (GA). This thesis provides a framework that uses a holistic approach to provide insights into the effects of using eco-mode of servers within the dynamics of virtual machine placement in data centers. This framework serves as a first step in parameterising the dynamics of a data center regarding its energy consumption and performance. The results show a potential energy reduction of up to approximately 20% with negligible impact on a data center’s performance. This result occurs when applying the best fit decreasing algorithm and having a server with an energy-efficient eco-mode. However, this thesis does not cover all parameters that play a role in the data center’s performance and energy consumption, so more research on this area is recommended.

The field of epidemiology encompasses a broad class of spreading phenomena, ranging from the seasonal influenza and the dissemination of fake news on online social media to the spread of neural activity over a synaptic network. The propagation of viruses, fake news and neural activity relies on the contact between individuals, social media accounts and brain regions, respectively. The contact patterns of the whole population result in a network. Due to the complexity of such contact networks, the understanding of epidemics is still unsatisfactory. In this dissertation, we advance the theory of epidemics and its applications, with a particular emphasis on the impact of the contact network. Our first contribution focusses on the analysis of the N-Intertwined Mean-Field Approximation (NIMFA) of the Susceptible-Infected-Susceptible (SIS) epidemic process on networks. We propose a geometric approach to clustering for epidemics on networks, which reduces the number of NIMFA differential equations from the network size N to the number m << N of clusters (Chapter 2). Specifically, we show that exact clustering is possible if and only if the contact network has an equitable partition, and we propose an approximate clustering method for arbitrary networks. Furthermore, for arbitrary contact networks, we derive the closed-form solution of the nonlinear NIMFA differential equations around the epidemic threshold (Chapter 3). Our solution reveals that the topology of the contact network is practically irrelevant for the epidemic outbreak around the epidemic threshold. Lastly, we study a discrete-time version of the NIMFA epidemic model (Chapter 4). We derive that the viral state is (almost always) monotonically increasing, the steady state is exponentially stable, and the viral dynamics is bounded by linear time-invariant systems. In the second part, we consider the reconstruction of the contact network and the prediction of epidemic outbreaks. We show that, for the stochastic SIS epidemic process on an individual level, the exact reconstruction of the contact network is impractical. Specifically, the maximum-likelihood SIS network reconstruction is NP-hard, and an accurate reconstruction requires a tremendous number of observations of the epidemic outbreak (Chapter 5). For epidemic models between groups of individuals, we argue that, in the presence of model errors, accurate long-term predictions of epidemic outbreaks are not possible, due to a severely ill-conditioned problem (Chapter 6). Nonetheless, short-term forecasts of epidemics are valuable, and we propose a prediction method which is applicable to a plethora of epidemic models on networks (Chapter 7). As an intermediate step, our prediction method infers the contact network from observations of the epidemic outbreak. Our key result is paradoxical: even though an accurate network reconstruction is impossible, the epidemic outbreak can be predicted accurately. Lastly, we apply our network-inference-based prediction method to the outbreak of COVID-19 (Chapter 8). The third part focusses on spreading phenomena in the human brain. We study the relation between two prominent methods for relating structure and function in the brain: the eigenmode approach and the series expansion approach (Chapter 9). More specifically, we derive closed-form expressions for the optimal coefficients of both approaches, and we demonstrate that the eigenmode approach is preferable to the series expansion approach. Furthermore, we study cross-frequency coupling in magnetoencephalography (MEG) brain networks (Chapter 10). By employing a multilayer network reconstruction method, we show that there are strong one-to-one interactions between the alpha and beta band, and the theta and gamma band. Furthermore, our results show that there are many cross-frequency connections between distant brain regions for theta-gamma coupling.@en

Multi-access Edge Computing (MEC) is a concept brought up by ETSI and it places computing, storage, processing and network resources into MEC hosts and places these MEC hosts as close as needed to the telecom network edge in order to reduce service latency and bandwidth usage. For self-driving vehicles, streaming video and real-time gaming, the devices involved (e.g. vehicles, cellphones, etc.) might not have enough capabilities to perform all the computations and might not have sufficient storage capacity; MEC can be used here for offloading data computations and content caching. To enhance service quality and user experience, MEC hosts and MEC applications should be located close(r) to the end-users, which increases the number of handovers between MEC hosts to maintain MEC service continuity for mobile end-users as well as the costs for the telecom operators. Therefore, a balance needs to be found. Consider the fact that mobile UEs need MEC service handovers to maintain service continuity and handovers may cause service interruptions which can cause severe degradation to MEC service qualities and user experience, hence the number of handovers between MEC hosts experienced by end-users should be minimized. To find a suitable deployment of MEC hosts and MEC applications in order to minimize the number of handovers, three greedy algorithms and two heuristic algorithms are introduced, implemented, tested, compared and analyzed in this thesis to see which identifies the deployment mechanism that has the smallest number of handovers. When it is time for a mobile UE to connect to a new MEC host and there are multiple potential choices of the new MEC host, the most suitable one for the UE needs to be determined dynamically according to the real-time condition of each possible MEC host. To achieve this, reinforcement learning is considered. Three different reinforcement learning algorithms based on SARSA learning and Deep Q Network are introduced, implemented, tested, compared and analyzed in this thesis. Furthermore, a decision-making mechanism is designed to cope with exceptional situations where the required service quality cannot be guaranteed.

This thesis’ research concerns the time-dynamics of a complex geographical network of municipalities, i.e. the Dutch Municipality Network over the period 1830-2019. By analysing 190 years of socioeconomic statistical data and applying contemporary tooling from network science and geographic information systems (GIS), the findings from this research can provide a new approach and supportive methods for policymakers, statistical offices, researchers and businesses (to decide when and where to invest).

Narrowband Internet-of-Things (NB-IoT), recently introduced by 3GPP, is a relevant Radio Access Technology (RAT) solution for deployment within smart grids, the electricity grids of the future, due to the need to provide low-cost connectivity to a large number of smart meters installed in households. Outage Restoration and Management (ORM), a smart grid use case, involves the smart meter User Equipment (UE) sending a notification message to the utility upon the detection of a loss or restoration of power. ORM is an effective way for utilities to quickly detect, localise and restore a power outage. However, depending on the extent of the power outage, a near simultaneous network access by multiple UEs may occur, leading to resource congestion, particularly of the so-called ‘random access channel’. This may impact the reliability, i.e. the percentage of total notifications successfully delivered within a certain transfer delay target, and in turn, the accuracy of the power outage localisation. Consequently, the maximisation of the ORM reliability performance for a technology like NB-IoT becomes a challenge, given that such use cases, though relevant, were not considered in its design phase. The main goal of this thesis is the optimisation of the NB-IoT network configuration, with a focus on packet scheduling, in order to maximise the ORM reliability performance. To this extent, a system-level simulation model is developed and implemented, incorporating realistic characteristics of energy distribution and mobile networks in four different environments (rural, suburban, urban and dense urban), the traffic characteristics of ORM and the relevant 3GPP specifications of NB-IoT. Additionally, a set of candidate time-frequency domain packet schedulers are proposed. A sensitivity analysis of key network configuration components is performed for a set of power outage scenarios i.e. network loads, the associated optimisation trade-offs are highlighted and a near-optimal network configuration is derived. Based on the sensitivity analysis, a proposed scheduler which prioritises UEs based on a combination of the Earliest Due Date First (EDDF) and Shortest Processing Time First (SPTF) principles, and assigns each UE a single uplink subcarrier with a subcarrier spacing of 3.75 kHz, performs best amongst all the candidate schedulers. Furthermore, the achieved reliability performance is close to 100% for all the considered power outage scenarios in the rural and dense urban environments. In the suburban and urban environments, close to 100% reliability is achieved for the majority of the power outage scenarios.

In modern society, critical operations, such as emergency response and public safety, rely on communication systems, in this context also referred to as mission critical systems. These systems must meet strict availability requirements, since any failure can lead to severe consequences, including loss of life. Traditionally, dedicated private communication systems, like local Push-to-Talk systems, were used for such critical operations, but there is a growing shift towards utilizing public 4G and 5G mobile networks to achieve better coverage, higher data speeds and more innovative features at a lower cost. With the increasing complexity and vast amount of data from the network, automation and artificial intelligence (AI) are now becoming essential tools in these communication systems to efficiently manage data, configure networks in real-time, and effectively handle alarms. The use of AI can improve the end-to-end availability of mission critical systems, ensuring communication during critical situations. The main goal of this research is to investigate whether and how the use of AI can improve the end-to-end availability of mission critical systems, with a specific focus on the Mission Critical Push-to-Talk (MCPTT) system of KPN, which is using the public 4G and 5G network. Currently, the KPN MCPTT system is being used with a relatively limited number of users. However, the vision for MCPTT extends beyond its current implementation, aiming to scale up this service. With an increasing number of users, using automation and AI is essential for optimizing and managing the complexities of this mission critical communication system. The implementation of AI in the MCPTT system follows a systematic approach, starting with the independent analysis and monitoring of system specific elements. By focusing on these elements and using data such as Call Detail Records (CDR) and log data, insights into the system's behavior can be obtained. Through collaboration with system experts, AI algorithms can be trained to effectively detect anomalies, thereby enhancing the overall availability of the MCPTT system. Looking ahead, the integration of real-time data becomes crucial for proactive monitoring. Establishing a streamlined data pipeline facilitates the flow of real-time information, offering a comprehensive overview of system performance and enabling swift anomaly detection. It is concluded that the monitoring of individual system elements with the use of AI is a first step towards improving the end-to-end availability. In order to ensure the correct use of AI throughout the complete cycle, it is crucial to look at explainability, safety, and data quality. These points should be included at each stage of the AI process. By ensuring explainability, system experts can gain insights into the decision-making process of the AI algorithms. By using safety mechanisms, potential risks and vulnerabilities can be mitigated. Maintaining data quality is essential to achieve accurate outcomes.

The main goal of the thesis is to investigate how to optimize Quality of Experience (QoE) of users using applications over satellite links by application aware load balancing capabilities of SD-WAN. SES (Commercial satellite operator) customers want to use applications over satellite links that have high latency and are often more congested than terrestrial networks which results in lower Quality of Experience (QoE) of users. The applications have been designed and optimized for terrestrial networks, not for satellite networks. Thus, SES wants to use its hybrid (MEO/GEO) satellite network and application aware routing capabilities of SD-WAN to prioritize and steer traffic at the application layer based on intent and business rules and enforced via policy for appropriate QoE. In the thesis, work is carried out in two parts: Firstly, experiments in lab to perform performance measurement of selected widely used applications over the different satellite links (GEO, MEO and LEO). Then performance of video applications over MEO link in different congestion scenarios (Unidirectional and Bidirectional Congestion) was measured. In order to improve the performance of video applications load balancing mechanism was defined to optimize QoE of the user. Secondly, a simulation model emulating a future SD-WAN scenario on Simulink, which is used to measure QoE of multiple users is designed. A load balancing mechanism which not only optimizes the QoE for multiple users but is also a cost effective alternative to manage the QoE is proposed. It was concluded that applications belonging to the same category have varied performances in different congestion scenarios on satellite links. Hence, each application has its performance, variation and should be dealt with accordingly. Identifying performance thresholds in different scenarios is essential to derive load balancing mechanisms to improve QoE and optimize the cost. Key applications that drive the behaviour of experience should be identified (which differs in each use case and for different customers) and steered accordingly to the best possible link so that overall QoE could be improved. Recommendations on the designing of policies for different use cases and overall development of SD-WAN as a product have also been presented in the thesis.

This thesis titled ´Study of 5G Roaming Security´ investigates the potential network vulnerabilities of 5G roaming reference points. The 5G Non-Standalone (NSA) is already being deployed in different countries across the world. With 3G becoming obsolete, mobile communication will primarily depend on 4G and 5G-NSA. In the later stages of the 5G rollout, 5G Standalone (SA) deployment will also take place gradually. Since 5G will support various use cases such as connected vehicles, smart farming, and smart healthcare, the number of connected devices will eventually increase. This would mean more data traffic generation compared to the LTE. In such a scenario, the privacy protection of the User Plane becomes highly significant. This thesis work provides a randomization-based security solution that would make the Standalone 5G reference points more robust to interception attacks. The goal is to implement a negotiation-based randomization solution over N9, N3, and N32 as these reference points cross the HPLMN boundary. This solution will be a part of the GTP-U header and can be easily implemented by modifying the existing signaling procedures. Random bytes will be added to the GTP-U header before the start of the payload. The idea of adding randomization bytes has been extended to include TCP-based randomization and IMS-based randomization. The TCP-based randomization also includes two different algorithms for the addition of random bytes. After analyzing the User Plane security, the vulnerability analysis of SEPP was undertaken, to understand the vulnerabilities that can be a threat to the network infrastructure. A vulnerability assessment matrix was made and high-risk vulnerabilities were highlighted along with a few precautionary steps. The implementation details and architectural changes for the implementation of GTP and TCP-based randomization are provided. The randomization is useful in masking the signature distribution of an application's packet length and can be a powerful protection mechanism against data traffic analysis attacks.

Accurate capacity planning is essential to ensure uninterrupted services and network stability through peak hours for the transport core network of KPN. This involves a trade-off between minimizing the risks of capacity shortages and costs of capacity expansions. High network loads are occurring more frequently and their magnitude is increasing. This necessitates measures to foresee high load situations before network capacity is surpassed. Currently, planning is based on manual predictions that lack substantiation. This research aims to improve network capacity planning by development of a forecast for the next year. An analysis of the daily maximum traffic data of the transport core is performed, to determine the most suitable models for the prediction of network traffic. The data analysis, employing time series decomposition, revealed non-stationary trends and annual seasonality; traffic decreases throughout the summer and increases in the winter. An upward trend in the frequency and intensity of traffic peaks, highlights the growing demand and shifts in usage behavior. The extreme traffic peaks in the historical data were correlated to F1 race days and other anticipated events. Two algorithms that integrate exogenous variables were assessed to predict the extreme values. The models either yielded inaccurate traffic predictions or encountered challenges in interpretability and pattern recognition, with the limited amount of data available. In response to these limitations, a decomposed forecast was created that predicts the trend and seasonality. Furthermore, Extreme Value Analysis (EVA) was implemented to address the extreme values in the data. The final prediction framework combines the decomposed forecast with EVA for the next six quarters and outperforms the other models. The model effectively captures extreme values and provides insights into the maximum expected peaks and risk levels. The substantiated forecasts of the EVA model and the manual predictions yielded comparable results. However, the EVA model provides better insights into the likelihood of exceeding specific traffic values, which enhances capacity calculations and precision. The prediction framework has been integrated into the business interface of KPN, which marks the initial step in the automatization of short-term capacity planning. The research insights emphasize the intricate nature of accurate prediction of future demand and advocate for scalable solutions beyond building new capacity. These solutions range from short-term mitigation to long-term strategies designed to alleviate high network loads. They underscore the importance of the implementation and integration of dynamic decision-making within a digital twin of the network to ensure sustained effectiveness.

Networks are becoming ever more important in today's highly interconnected society, from telecommunication networks and social networks to power grids and the Internet. The field of Network Science seeks to uncover structure within the complex topologies of networks and the processes that govern their link formation. Many methods and models in the field are founded on link-formation principles that are driven by similarity, drawing inspiration from social network theory. In this dissertation, we discuss various network representations based on similarity, and we introduce and illustrate an alternative link formation principle that is based on complementarity. The first part of this dissertation focuses on clustering the nodes of a network or community detection. Here, the nodes of a network are partitioned into several clusters and the objective is to precisely determine the cluster memberships based on only the network topology. Many clustering methods assume that the true number of clusters is known a priori. In Chapter 2, we investigate how exactly to find this number of clusters for a given graph. We discuss several modularity maximization and spectral clustering methods, and we outline how they can be used to find the number of clusters. We compare the performance of several different algorithms by evaluating these methods on benchmark graph models where the ground truth clusters are known. In the second part, we explore network representations in the hyperbolic space. In Chapter 3, we extend the 2-dimensional random hyperbolic graph model to a hyperbolic space of arbitrary dimensionality. Our rescaling of the model parameters and variables casts the random hyperbolic graph model of any dimension to a unified mathematical framework, such that the degree distribution is invariant to the dimensionality of the space. We analyze the different connectivity regimes of the model and their limiting cases. In Chapter 4, we describe how hyperbolic graphs are built on a connection principle based on similarity, and we identify a class of real-world networks in which the links are driven by principles of complementarity rather than similarity. We propose a framework for embedding complementarity-driven networks into hyperbolic space and we describe the ensuing complementarity random hyperbolic graph model. In Chapter 5, we further investigate the topological properties of the complementarity random hyperbolic graph. The third and final part of the dissertation centers on semantic networks, which describe semantic relations between words or concepts. In Chapter 6, we systematically analyze the topological properties of a large, multilingual dataset of semantic networks. Our investigation covers both universal and language-specific structural properties of these networks. We examine the roles that the connection principles of similarity and complementarity play in their link formation, and we discuss how a deeper understanding of these organizing principles benefits applications in natural language processing.@en