The development of quantum computing poses a significant threat to currently deployed cryptographic primitives. To address this challenge, we design and implement a system to communicate and store data using post-quantum secure algorithms for a distributed blockchain environment. Specifically, we combine the Crystal-Dilithium digital signature scheme and an Updatable Encryption scheme based on the Learning With Errors problem in the context of a Hyperledger Fabric network. The Crystal-Dilithium scheme ensures authenticity and integrity of transmitted data, while the Updatable Encryption scheme provides confidentiality and integrity to transmitted data as well as allowing key rotation and ciphertext updates without requiring data re-encryption from scratch. An end-to-end prototype of the system is developed, including client-side modules, a Hyperledger Fabric gateway and smart contract modules that can be deployed on a Hyperledger Fabric network, with all components implemented in Python and Golang. The system is evaluated through functional integration tests and performance benchmarks. Results demonstrate that the Crystal-Dilithium scheme achieve sub-second execution for key generation, signing, and verification for multiple different parameter sets. For the Updatable Encryption scheme, encryption, decryption, and ciphertext updates exhibit linear scalability in time complexity with respect to the size and dimensions of input parameters, while token generation emerges as the most computationally demanding step in the algorithm. Additional experiments highlight the practical limits of repeated key rotations due to accumulated error growth.

The energy consumption of mobile networks, particularly the 5G Radio Access Network (RAN), is becoming a growing concern due to its environmental and economic implications. As the demand for higher data rates and low-latency services intensifies, 5G networks, integrating macro cells and small cells, are emerging as critical infrastructures. Although small cells improve coverage and capacity, their increased deployment could lead to a significant rise in the overall power consumption of 5G systems.
Current small cell selection strategies by User Equipment (UE), although effective in some cases, do not fully account for the dynamic nature of traffic conditions and the specific data requirements of users. Moreover, current techniques such as the maximum Signal-to-Interference-plus-Noise Ratio (max-SINR) and Cell Range Expansion (CRE) purely consider the signal strength of the link between the user and the base station to allocate users to the base station. However, this leads to inefficient utilization of base station resources and uneven distribution of load, causing congestion at some base stations while leaving others underutilized.
In order to address these gaps, this thesis proposes a Traffic Distribution Orchestrator (TDO) to manage the distribution of users between cells dynamically, and optimize energy efficiency without compromising network performance. The proposed cell selection model developed in this thesis also accounts for user mobility and dynamic traffic conditions. The model estimates instantaneous power consumption and informs a real-time algorithm user equipment-base station (UE-BS) association algorithm to dynamically allocate users to the cell which will enhance the energy efficiency of the network while ensuring the required Quality of Service (QoS) requirements. Complementing this, an adaptive sleep mode mechanism puts underutilized small cells in a low power mode and reactivates them when demand rises, using hysteresis to prevent state flapping and reduce idle power.
Through MATLAB simulations, the effectiveness of the model and algorithm is validated, with results indicating a significant reduction in network power consumption in heterogeneous 5G deployments. The proposed UE-BS association algorithm is compared with the max-SINR, CRE and a representative association method from the previous studies, whereas the proposed adaptive sleep mode mechanism is compared with fixed threshold sleep mode mechanism under both bursty and steady traffic. The proposed UE-BS association algorithm combined with the adaptive sleep mode mechanism reduces total network power consumption relative to baseline strategies. This research contributes to the advancement of sustainable 5G network architectures and offers insights into energy efficiency optimization in real-world scenarios.

With the rapid development of industrial systems, the demand for stability, reliability, and robustness has become increasingly critical. Fault detection has emerged as a key research area, aiming to prevent unexpected failures and performance degradation. Recent advances in feature extraction techniques and machine learning have enabled the development of intelligent, autonomous fault detection systems.

This thesis proposes two Transformer-based models for motor fault detection. The first is a supervised classification model that incorporates discrete wavelet transform (DWT) to decompose time-series signals into multi-scale components, which are then processed by Transformer-based architectures to extract features for classification. Two structural variants are explored: one using masked attention over concatenated coefficients, and another employing upsampling and linear attention for efficient fusion. The second approach is an unsupervised forecasting-based model, where only normal samples are used for training. At inference time, samples are classified based on whether their forecasting error exceeds a threshold determined via ROC curve analysis on a validation set.

Experiments conducted on the JKU and CWRU datasets demonstrate the effectiveness of both approaches. The classification-based method achieves high accuracy in distinguishing between fault types, while the forecasting-based method shows strong robustness to previously unseen fault categories without retraining. The findings indicate that the models are capable of capturing informative temporal patterns for fault detection, showing promise for further exploration in real-world settings.

Microcontroller-based neural network inference faces significant RAM constraints, hindering performance and deployment. One of the main constraints is the peak memory usage, which is essential for conducting deep learning inferences with low latency. To address this, previous researchers have developed peak memory estimators to estimate the Peak Memory Usage, which could be used by inference-time optimization techniques like pruning to tackle the RAM constraint. But many of the peak memory estimators used by current state-of-the art frameworks like µNAS and TinyEngine produce underestimation or overestimation, reducing the reliability of model decisions made under RAM constraints. Underestimation often arises from failing to account for all components contributing to peak memory usage, while overestimation can occur when extra memory overheads irrelevant in MCU-specific inference scenarios are incorrectly included. In this paper, we propose our peak memory estimator, which estimates the peak memory usage of deep learning inference at the operator level and can accurately estimate the peak memory usage of a deep learning model during inference. The experiments show that our method achieves more accurate peak memory predictions across multiple MCU platforms and can be effectively integrated with pruning strategies to produce better model compression that satisfies both memory and accuracy constraints. Specifically, on a benchmark of 150 models, our method achieved an average estimation error margin of only 0.9%, significantly outperforming µNAS, which exhibited an average error margin of 61.7%.

The sixth generation (6G) of mobile networks promises transformative capabilities in terms of, amount others, higher data rates, lower latency and ubiquitous coverage, but achieving these goals sustainably poses significant challenges. A promising solution lies in Cell-Free massive Multiple-Input Multiple-Output (CF-mMIMO) networks, where a dense deployment of geographically distributed Access Points (APs), coordinated by one or more Central Processing Units (CPUs), cooperatively serve User Equipments (UEs). While CF-mMIMO networks offer improved spectral efficiency and more spatially uniform service, their dense deployments can result in high energy consumption. As MNOs typically design their networks such that Quality of Service (QoS) targets are met during peak hours, the network is left significantly over-dimensioned during off-peak hours. This thesis addresses this inefficiency by proposing a low-complexity, heuristic Sleep Mode Management (SMM) algorithm that reduces energy usage by switching off unneeded APs while maintaining acceptable QoS and without compromising coverage.

The proposed SMM algorithm supports multiple AP power states: active, light sleep and deep sleep. It incorporates realistic transition times between these states. Relying solely on practically available information such as long-term Channel State Information (CSI) and previously achieved data rates, the algorithm dynamically decides which APs can be temporarily put into a sleep mode. Importantly, it ensures population coverage is maintained and it preserves UE QoS based on a 10th UE throughput percentile target.

The proposed SMM algorithm is evaluated using a system-level simulator that models a realistic scenario based on the city center of Amsterdam, including lamppost-based AP deployments and a realistic basis for the spatial traffic distribution and daily traffic fluctuations. Simulation results demonstrate that the proposed SMM algorithm reduces the daily energy consumption of a CF-mMIMO network by up to 17.11\% with the best overall configuration. If the parameters of the SMM algorithm are allowed to be adaptively tuned to the traffic load, the daily energy consumption can be reduced by up to 21.54%. This thesis not only contributes a novel SMM algorithm but also provides guidance for AP deployment strategies in 6G CF-mMIMO networks, determining that a higher number of lower-antenna-count APs can yield better QoS than fewer, more higher-antenna-count APs at the cost of energy efficiency.

We present a machine learning framework aimed at forecasting Starlink (LEO satellite) network performance at fine spatiotemporal resolution. Our approach combines MLab crowdsourced measurements, weather and forecast features, and dynamic satellite density to predict packet loss, jitter, latency, and throughput. We introduce a composite Weather Index and real-time satellite density per location, and train robust ensemble models with anomaly filtering and median aggregation. Our best models achieve good predictive results with less than 17 ms for latency, and 35 Mbps for throughput. Latency is reliably predictable with meteorological and satellite context, while packet loss and jitter remain challenging. Predictions are limited to periods close to the training data and our results establish a reproducible baseline for short-term, weather-aware Starlink network forecasting.

The rise of Low-Earth-Orbit (LEO) satellite networks, such as Starlink, has transformed global connectivity, enabling high-speed internet access in previously underserved regions. However, existing research lacks a unified framework to evaluate and compare the performance of LEO ISPs against terrestrial alternatives using heterogeneous measurement datasets. In this work, we present a methodology for harmonizing and standardizing passive internet measurements from M-Lab’s NDT7 and Cloudflare’s AIM datasets, implemented in the form of the Global Telemetry System. These sources are integrated through schema unification, filtering, and normalization to produce a reproducible and geographically comprehensive telemetry dataset. We introduce a server-based filtering approach to mitigate geographic and routing biases, and we evaluate aggregation methods to align measurement distributions across datasets. Our results demonstrate the dataset integration methodology preserves key distributional properties, enabling fair and statistically consistent merging of measurements from the two sources. This work represents a first step toward a scalable and extensible telemetry infrastructure for assessing next-generation global internet services.

Low Earth Orbit (LEO) satellite constellations, particularly SpaceX’s Starlink, have quickly gained popularity and have become a viable alternative to traditional terrestrial Internet Service Providers (ISPs) in recent years. However, due to their novelty and unique architecture, research into their performance is limited, especially one comparing LEO and terrestrial internet. This paper will demonstrate how user-initiated active measurements can be used to both gather new data about LEO internet and assess and compare the performance of individual networks. First, performance metrics that reflect typical internet usage scenarios, such as web browsing and video streaming, are chosen. Next, a test suite is developed to collect data about one’s network. It also augments this data with location information to aid in later comparison. This data can then be used for comparisons between individual networks as well as in further research. The last step integrates the suit developed into a web based platform that aims to provide a wide variety of information about Starlink’s performance, architecture and allow users to gauge the potential benefits of transitioning from terrestrial to LEO internet could provide them.

Quantum computing holds the potential to solve problems that are intractable for classical systems. However, the physical realization of large-scale quantum systems remains a challenge due to the difficulty of scaling qubit counts. Distributed Quantum Computing (DQC) offers a promising solution by interconnecting multiple Quantum Processing Units (QPUs), effectively increasing the number of usable qubits. This interconnection introduces additional operations and exacerbates the complexity of qubit routing and entanglement management during circuit execution. While a reinforcement learning (RL) approach by Promponas et al. shows promise for qubit routing and EPR management in distributed quantum computing (DQC) environments, it suffers from inconsistent performance and is unable to reliably compile larger circuits. To address these limitations, we introduce a novel action space that allows direct operations between arbitrary qubit pairs, rather than restricting interactions to neighbouring qubits. While this significantly reduces solution depth, it increases the size of the action space, posing scalability challenges. To address this, we propose a novel neural network architecture that computes Q-values for qubit pairs based on the values of their individual qubits, considerably reducing the number of trainable parameters. Additionally, we extend the masking strategy to eliminate sub-optimal actions, effectively constraining the branching factor and accelerating learning. Together, these enhancements enable the agent to compile larger circuits with improved speed and consistency, significantly outperforming the baseline. Overall, our contributions result in better performance and reduced training time, marking a step forward in scalable quantum circuit compilation for distributed quantum systems.

Gunshot detection plays a critical role in protecting African wildlife and reducing illegal poaching activities. The decline of keystone species disrupts ecosystems and inhibits forest CO₂ absorption, contributing to climate change. To support conservation efforts, this thesis contributes to the development of an embedded acoustic surveillance system that detects gunshot sounds and locates their sources, enabling rangers to respond in real time. However, realizing such a system is challenging due to limitations in data, resources, and infrastructure.

This thesis proposes a lightweight convolutional recurrent neural network, combining depthwise separable convolutions (DSConv) and a gated recurrent unit (GRU), designed to detect the onset of gunshot sounds for trilaterating the shooter's position. The model was trained on a real-world gunshot dataset and optimized for deployment on the ultra-low-power STM32U5 microcontroller. A series of incremental experiments on architectural design, data manipulation, and feature selection aimed to improve performance and efficiency.

To evaluate detection performance under class imbalance, independent of confidence thresholds, a novel onset-based area under the precision–recall curve (AUPRC) metric was proposed. Computational cost was evaluated through STM32U5 inference benchmarks. Experimental results showed that time-shift augmentation provided the largest performance gain, followed by modest improvements from regularization techniques. Class rebalancing and background noise augmentation had minor effects. Replacing standard convolutions with DSConv substantially improved efficiency. Finally, using ∆mel-frequency cepstral coefficients (∆MFCCs) as input features further improved both performance and efficiency.

Overall, the final ∆MFCC DSConv-GRU model outperformed the quasi-DenseNet baseline in F1-score (+2.6%) while reducing multiply-accumulate operations (-96.9%), RAM usage (-95.8%), and runtime (-97.3%). These improvements enabled real-time inference on microcontrollers, demonstrating that a lightweight deep learning model can perform effectively under strict resource constraints. Hence, this work provides a foundational step toward future embedded gunshot detection systems for wildlife monitoring and anti-poaching applications.

Recommender systems are widely used in modern lives and contribute to many industries. Therefore, methods to evaluate and improve them are important. Nowadays, much research has been done to improve the system aspects such as algorithms. However, user experiences are not only affected by the systems but heavily rely on the context when using the systems. Therefore, the research on user aspects to understand their experiences is as important. This study contributes an approach that uses collaborative reflection to find insights into users' experiences with recommender systems. Using this approach, this study presents the influences of context on user experiences with recommender systems. This study investigates the importance of situational and personal contexts like mood, time, and location in shaping user satisfaction with recommendations. The research adopts a method based on collaborative reflection, where participants engage in tasks using their YouTube watch history, paired with another individual for real-time discussion. By analyzing contextual influences and the values users wish to achieve, the study identifies key patterns in user behavior and insights into personal preferences. This research not only contributes to the evaluation of recommender systems but also highlights the need for systems to align with both the goals of users and broader societal values. The usability of the proposed method was tested to be successful in crowdsourcing, yielding practical implications for future evaluation and improvements of recommender systems.

Federated Learning (FL) is a distributed machine learning approach that enhances data privacy by training models across multiple devices or servers without centralizing raw data. Traditional FL frameworks, which rely on synchronous updates and homogeneous resources, face significant performance challenges in real-world deployments. These challenges include handling heterogeneous data distributions (non-IID) and varying computational resources across clients, leading to performance degradation. To address these issues, asynchronous FL frameworks have been proposed, but they introduce new complexities such as communication latency and workload management across geo-distributed servers.

This paper focuses on the impact of network latency and non-IID data distributions on asynchronous multi-server FL systems. We propose and evaluate three methods to mitigate the adverse effects of data heterogeneity on model accuracy: (1) transferring clients between servers; (2) sharing clients among servers so that they alternatively train their models; and (3) sharing clients among servers with model averaging. Our contributions include a reproducible experimental framework for multi-server FL, strategies for optimizing client-server interactions, and an analysis of the effectiveness of these strategies in reducing the impact of non-IID data distributions. Experimental results demonstrate that our methods can reduce training time by up to 85.67%, decrease the number of updates required by 85.82%, and improve accuracy by 4.815% in heterogeneous environments, compared to Spyker, a state-of-the-art multi-server FL algorithm.

Visible Light Communication (VLC) leverages the visible light spectrum to establish wireless communication, offering advantages such as broader bandwidth, and reduced energy consumption compared to traditional radio frequency methods. VLC offers two main approaches: passive and active. Passive VLC takes advantage of sunlight, which is pervasive and highly power-efficient. However, its reliability can be affected by weather conditions and the absence of sunlight at night. On the other hand, active VLC which uses artificial light sources like LEDs, provides more consistent performance but is not power-efficient when sunlight is available. For example, during the day when ample sunlight could be used for passive VLC, turning on a light bulb for active VLC is unnecessary and wasteful.

This thesis tackles these challenges by combining the best of active and passive systems to create an even more power-efficient and reliable system. It addresses two key problems in passive VLC: reducing the power consumption of passive VLC transmitters and enhancing the reliability of passive VLC links through a hybrid system. By replacing the FPGA-based controller with a low-power microcontroller, the power consumption of the Digital Micro-mirror Device used for sunlight modulation was significantly reduced from 1.3W to 36.85mW, while achieving a data rate of 25 kbps with a bit error rate (BER) of less than 1% at a distance of 25 cm. Its maximum range was determined to be 75 cm at 10 kbps. Additionally, integrating an LED component into the passive VLC communication link improved reliability in varying ambient light conditions. The hybrid system demonstrated enhanced performance in low ambient light scenarios, ensuring a BER below 1% regardless of ambient light conditions. In high ambient light scenarios, the LED can be dimmed or turned off, conserving power and making the system more efficient than a purely active VLC system. This thesis contributes to the advancement of energy-efficient and reliable VLC technologies, paving the way for their broader adoption.

Federated Learning has gained prominence in recent years, in no small part due to its ability to train Machine Learning models with data from users' devices whilst keeping this data private. Decentralized Federated Learning (DFL) is a branch of Federated Learning (FL) that deals with clients directly communicating with each other as opposed to using a central server. Client mobility describes how users' devices move in the real world, and its effects on the learning performance of Hierarchical Federated Learning (HFL) systems have been found to be significant. However, the effects of client mobility on DFL systems have not been explored. In this work, we fill this research gap. First, we develop a model that can describe client mobility in a DFL system. Then, using synthetic datasets, we show that client mobility has a positive impact on learning performance, which we quantify. Moreover, we show that there is a disparity in learning performance between high-mobility and low-mobility clients when using a baseline model aggregation algorithm. To address this disparity, we propose a new mobility-aware model aggregation algorithm. Our experimental results on synthetic datasets show that our solution reduces the disparity in learning performance between high- and low-mobility clients in the scenarios where this disparity is greatest, with no appreciable downsides in global learning performance.

Federated Learning (FL) systems often suffer from high variability in the final model due to inconsistent training across distributed clients. This paper identifies the problem of high variance in models trained through FL and proposes a novel approach to mitigate this issue through scheduling simulations subject to precedence constraints. By effectively scheduling the execution of client tasks and parameter server updates, we aim to reduce the variance in the final aggregated model. Through a series of experiments, we demonstrate that our proposed scheduling method significantly reduces model variance, while not impacting the time of simulation drastically. Additionally, we propose 2 algorithms to solve the problem of scheduling under precedence constraints - Ant Colony Optimisation, and an Evolutionary Algorithm - to minimize the makespan of simulations.

Federated learning (FL) is a machine learning paradigm where private datasets are distributed among decentralized client devices and model updates are communicated and aggregated to train a shared global model. While providing privacy and scalability benefits, FL systems also face challenges such as client and data heterogeneity, where the training resources and different datasets are non-Independent and Identically Distributed (non-IID). When testing novel FL algorithms or configurations, simulators are used to create controlled environments without needing costly deployments. However, it is important to understand how representative FL simulators are of real-world deployments, and what steps can be taken to bridge the gap between theoretical results and practical implementations. In this paper, we investigate the effects of incorporating traces from a pseudo-real heterogeneous FL deployment in simulated environments. We compare four non-IID attributes, including batch sizes, local epochs, data volume, and data labels, to determine the most influential factors for reproducing the deployment results in simulations. We show that there is an inherent difference between deployments and simulations, despite incorporating identical non-IID conditions. Furthermore, we show that including non-IID data labels in simulations has the most significant impact on recreating the deployment outcome. We also demonstrate that incorporating the other mentioned factors has negligible impact, resulting in similar training performance compared to fully IID simulations. Our results are derived from a 20-client single-server synchronous FL configuration, and additional research is necessary to confirm our findings for larger-scale systems.

Federated Learning is a machine learning paradigm where the computational load for training the model on the server is distributed amongst a pool of clients who only exchange model parameters with the server. Simulation environments try to accurately model all the intricacies of such a system. However, current simulators do not properly impose the concept of simulation time, leading to global model inaccuracies and difficulties of replicating reruns of the simulation, which is most prominent in the asynchronous scenarios. To this purpose, we propose a discrete-event simulator for the central server asynchronous case which timestamps all the events in the system prior to execution, reducing variability in client model updates on the server. We also introduce a log-structure used to keep states of the simulation, making client inspection possible based on time. We evaluate the proposed discrete-event simulator on the baseline simulator of Flower, reducing standard deviation amongst server model updates for 31.5% and improving accuracy with heterogeneous clients in the MNIST case for 3.3% on average.

Threshold signatures play a crucial role in the security of blockchain applications. An efficient threshold signature can be applied to enhance the security of wallets and transactions by enforcing multi-device-based authentication, as this requires adversaries to compromise more devices to recover the key. Additionally, threshold signatures can protect user privacy, for instance, by enabling anonymous transaction signing on behalf of a group of users sharing a blockchain wallet. This study conducts a comprehensive analysis of threshold signature schemes, identifying FROST as the premier choice due to its performance efficiency, improved energy consumption, and practical feasibility on mid-range IoT devices and smartphones as demonstrated through empirical testing. Furthermore, we introduce a protocol for integrating FROST with Hyperledger Fabric v3.0, aimed at enhancing IoT devices' ability to interact with blockchain networks through efficient transaction signing. Our experiments reveal that an IoT network of five devices, under optimal network conditions, can sign a transaction and commit it to the Hyperledger Fabric network within 3.2 seconds, leveraging a 2-second batch timeout configuration.

In the ever-evolving field of music technology, new solutions continue to emerge that enhance musical expression and creativity. This thesis introduces WaveTune, a novel lightweight hand gesture recognition system that enables real-time control of musical composition and performance through natural hand motions.

WaveTune utilizes Millimeter Wave radar technology to capture gesture data, combined with optimized deep learning techniques for real-time recognition. This provides an accessible and non-intrusive platform for gesture control that enhances privacy since no visual data is recorded. Users can dynamically select tracks and control musical parameters in real-time using expressive hand motions, integrating seamlessly with music software.

A key innovation of WaveTune is the development of an optimized gesture recognition model that achieves high accuracy for real-time music interaction while minimizing complexity. This is accomplished through novel optimizations to a state-of-the-art point cloud classification architecture, resulting in an efficient and tailored model for fluid musical control.

Furthermore, WaveTune promotes open-source collaboration by providing full access to code, configurations and datasets, inviting the community to build upon this system.