Publications

The book emerges from COST Action COREnet, a European research network initiated in 2021 that connects theory and practice in the field of migration and religious diversity. Over the course of several years, COREnet scholars from diverse disciplines and cultural contexts engaged in collaborative research, dialogical formats, and innovative methodologies to critically analyse how narratives influence migration experiences and to explore the role of religion and secular worldviews in these processes.

One of the most innovative aspects of the volume is its methodological emphasis on listening to the voices of migrants themselves. To facilitate this, the network developed and implemented the concept of “narrative cafés.” These dialogical spaces allowed migrants to share their personal stories in a supportive, interactive environment, thereby highlighting their lived experiences beyond abstract political or academic debates.

By bridging theoretical frameworks and practical concerns, Religious and Non-Religious Narratives on Migration offers scholars, policymakers, and practitioners critical tools for engaging migration in ways that are both academically rigorous and socially constructive.

The volume will be of particular interest to researchers in theology, sociology, philosophy, and education, as well as policymakers and organisations working in the fields of migration, integration, and interreligious dialogue.

In this paper, we experimentally demonstrate and analyze the performance of software-defined radio (SDR)-based hybrid optical wireless communication (OWC) links for indoor Internet-of-Things (IoT) networks. We propose all-optical hybrid visible light communication (VLC) and optical camera communication (OCC) using a single light emitting diode (LED) as a transmitter and photodiode (PD) and image sensor (IS) based receivers for simultaneous high-and low-speed data transmission. We present a testbed using an SDR platform, i.e., the ADALM-PLUTO, to transmit and receive high-and low-speed signals in the very-high frequency band and a media converter as part of an OWC link. We evaluate the performance of the proposed hybrid system employing a chip LED modulated with quadrature phase shift keying signal in terms of the bit error rate and the reception success rate Rrs. We show that the proposed scheme can provide an independent link performance, irrespective of the significant differences between the operating data rates of VLC and OCC links. The results show that for a link span of 1 m, we achieve (i) error-free detection for high-speed PD-based VLC; and (ii) 100 % Rrs at varying modulation frequencies for low-speed IS-based OCC.

This paper proposes a novel fingerprinting method by applying the controlled non-linearities of RF amplifiers in compression mode to generate robust signal signatures. By analyzing the distinct distortion patterns and harmonic content produced under compression, we define a signaling mechanism helping to generate a robust fingerprinting mechanism. Our approach presents an ability to generate confusion in eavesdroppers so ensuring high robustness and resilience against passive and active attacks. Numerical results demonstrate the consistency and robustness of the signal signatures. This method not only enhances security by integrating physical-layer properties, but may contribute to reduce the computational burden of traditional cryptographic techniques. Our findings indicate that using amplifier non-linearities for fingerprinting significantly improves the security and efficiency of wireless communication systems.

As 6G networks introduce increasingly diverse and complex applications, network slicing is a key enabling technology for partitioning network resources to meet these dynamic demands. However, efficiently managing and allocating these finite resources has become vital. This necessity drives the adoption of an open marketplace model. To address the business and technical complexities associated with such open marketplaces, this paper presents the demonstration of a non-fungible token (NFT)-enabled resource trading marketplace tailored for 6G network slicing. The proposed solution is implemented on an Ethereum-based blockchain system to assess its viability.

Internet of Things (IoT) technology has become ubiquitous with a broad spectrum of applications. This vast penetration entails formidable cyber-security for the stable operation of the associated systems. Most inexpensive IoT devices employ rudimentary cryptographic security mechanisms due to their resource-limited architecture. Radio frequency fingerprinting (RFF) is a physical layer security mechanism that leverages hardware impairments for authentication and device classification. To this end, its scope has been limited to academia owing to daunting challenges. In this work, an abridged overview of the state-of-the-art is provided, along with a summary of the challenges that hinder progress toward practical applications. The article culminates with a discussion on the intricacies of performance metrics in RFF and the direction for future research.

As the Internet of Things (IoT) permeates diverse application domains, ensuring the security of wireless networks has become increasingly critical. However, the constraints of resource-limited IoT devices render complex encryption impractical. Consequently, Radio Frequency Fingerprinting (RFF) has emerged as a promising avenue, leveraging unique device characteristics resulting from manufacturing nonlinearities. RFF enhances physical layer security by enabling device classification and authentication at IoT gateways. While deep learning (DL) aided RFF systems offer exceptional classification accuracy, their deployment on edge devices remains challenging to this end. Accordingly, there is a gap in the literature for efficient model exploration and implementation. This study proposes a lightweight Convolutional Neural Network (CNN) model using 1D convolutional filters to reduce inference latency. The model was applied to an open-source dataset comprising 30 LoRa devices. An evaluation was conducted to compare classification accuracy and inference latency using Short Time Fourier Transform (STFT) and Fast Fourier Transform (FFT) for preprocessing. Additionally, the performance of the proposed model was compared against a CNN model utilizing 2D convolutional filters. The model exhibited a significant reduction in inference latency with miniscule degradation in classification accuracy, addressing the identified gap, and propelling the academic discourse towards RFF for edge devices.

The rapid adoption of 5G networks, space networks, and Internet of Things (IoT) technologies in healthcare has significantly expanded the attack surface for cybersecurity threats. This evolving landscape demands robust defense mechanisms that can anticipate and neutralize sophisticated cyber-attacks. Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) technologies are pivotal in developing such advanced cybersecurity solutions. A critical factor influencing the effectiveness of these AI/ML models is the quality and diversity of the datasets used in their training. This paper presents a systematic review of various datasets used for AI/ML-based cybersecurity model training across multiple domains, with a focus on 5G networks, IoT healthcare, and space networks. By employing a structured Goal-Question-Metric (GQM) methodology and Quasi-Gold Standard (QGS) validation, we assessed the characteristics, applications, and limitations of real, synthetic, and hybrid datasets in enhancing cybersecurity measures. The review identifies key trends, gaps, and future research directions, highlighting the need for more diverse datasets, standardized benchmarks, and privacy-preserving techniques. Our findings offer insights into improving the resilience of AI/ML models for cybersecurity, guiding the development of more effective and adaptable defense strategies across emerging network technologies. 

Internet of Things (IoT) devices are becoming more and more common in homes, making the security and privacy of these increasingly important. To investigate how the users’ security and privacy practices can be improved, it is necessary to understand the current everyday practices and what impacts these. In an interview study, this paper reveals that users’ practices are primarily influenced by the convenience offered by the IoT device, and the motivation and the effort required from the user to make themselves aware of the security requirements and enact change within their IoT ecosystem.

Internet of Things (IoT) devices are becoming more and more common in homes, making the security and privacy of these increasingly important. To investigate how the users’ security and privacy practices can be improved, it is necessary to understand the current everyday practices and what impacts these. In an interview study, this paper reveals that users’ practices are primarily influenced by the convenience offered by the IoT device, and the motivation and the effort required from the user to make themselves aware of the security requirements and enact change within their IoT ecosystem.