As Virtual Reality (VR) systems increasingly incorporate biometric sensing and adaptive interaction techniques to enhance Quality of Experience (QoE), they also raise significant concerns regarding user privacy. While these innovations promise more immersive and personalised experiences, they often rely on the collection of highly sensitive data—such as physiological signals or behavioural patterns—without sufficient consideration of what this implies for privacy. In this position paper, we argue that privacy must be treated as a fundamental concern in QoE research, particularly as VR technologies transition from controlled experimental settings to real-world applications. We begin by outlining the current disconnect between QoE and privacy research. We then propose a research agenda aimed at reconciling QoE and privacy in VR, structured around the integration of privacy in all phases of the research process, from the research design to the analysis and dissemination of results. By embedding privacy into QoE research, we aim to foster the development of VR systems that are not only immersive and engaging, but also ethically responsible and socially sustainable.

The management of cellular networks and services has evolved due to the rapidly changing demands and complexity of service modeling and management. This paper uses intent-based networking (IBN) as a solution and couples it with contextual information from knowledge graphs (KGs) of network and service components to achieve the objective of service orchestration in cellular networks. Fusing IBN with KGs facilitates an intelligent, flexible, and resilient service orchestration process.We propose an intent completion approach using knowledge graph learning and a mapping model capable of inferring and validating the service intents in the network. Subsequently, these service intents are deployed using available network resources in a simulated fifth generation (5G) non-standalone (NSA) network. The compliance of the deployed intents is monitored, and mutual optimization against their required service key performance indicators is performed using Simultaneous Perturbation Stochastic Approximation (SPSA) and Multiple Gradient Descent Algorithm (MGDA). The numerical results show that the knowledge graph with Gaussian embedding (KG2E) model outperforms other distance-based embedding models for the proposed service KG. Different combinations of strict latency (SL) and non-strict latency (NSL) intents are deployed, and compliance is evaluated for increasing numbers of deployed intents against baseline deployment scenarios. The results show a higher level of compliance for SL intents to target latencies in comparison to NSL intents for the proposed intent deployment and optimization algorithm.

Information and Communication Technology (ICT) infrastructures are increasingly adopting software-based approaches, using paradigms such as Infrastructure as Code (IaC). This is aligned with the design of modern systems, such as 5G with the use of Virtual Network Functions (VNFs) and containerized services, combining the expertise and participation of various stakeholders from diverse cultural and educational backgrounds, potentially across different organizations. To create a common understanding of containerized networking infrastructures, we developed an ontology that allows a unified representation of core networking concepts. By enriching pre-deployment tasks with semantics, we universally represented and verified different network policies based on formal logic. To validate our approach, we constructed two knowledge graphs using open-source 5G Core Network implementations and demonstrated the potential of our approach by checking for compliance with automated reasoning. Using our defined rules, we extracted knowledge about non-compliant services and made inferences based on the well-defined concepts in the ontology. Our findings suggest that different network security policies can be integrated into each knowledge base, contributing to autonomic and cognitive management of future infrastructures. This can potentially provide more reliable IaC definitions, improve machine interpretation of IaC deployments, and assist human actors in making better-informed decisions.

This paper presents a novel approach to network management by integrating intent-based networking (IBN) with knowledge graphs (KGs); thus, creating a more intuitive and efficient pipeline for service orchestration. By mapping high-level business intents onto network configurations using KGs, the system dynamically adapts to network changes and service demands, ensuring optimal performance and resource allocation. This integration facilitates a deeper understanding of network states and dependencies, enabling predictive adjustments and real-time troubleshooting. We utilize knowledge graph embedding (KGE) to acquire context information from the network and service providers. The trained KGE model maps intents to services via service prediction and intent validation processes in the proposed intent processing pipeline. We evaluate the trained model for its efficiency in the service mapping and intent validation tasks using simulated environments and extensive experiments. The service prediction and intent verification accuracy ≥80% is achieved for the trained KGE model on a custom service orchestration intent knowledge graph (IKG) based on TMForum’s intent common model.

Intent-driven networks are an essential stepping stone in the evolution of network and service management towards a truly autonomous paradigm. User centric intents provide an abstracted means of impacting the design, provisioning, deployment and assurance of network infrastructure and services with the help of service level agreements and minimum network capability exposure. The concept of Intent Based Networking (IBN) poses several challenges in terms of the contextual definition of intents, role of different stakeholders, and a generalized architecture. In this review, we provide a comprehensive analysis of the state-of-the-art in IBN including the intent description models, intent lifecycle management, significance of IBN and a generalized architectural framework along with challenges and prospects for IBN in future cellular networks. An analytical study is performed on the data collected from relevant studies primarily focusing on the inter-working of IBN with softwarized networking based on NFV/SDN infrastructures. Critical functions required in the IBN management and service model design are explored with different abstract modeling techniques and a converged architectural framework is proposed. The key findings include: (1) benefits and role of IBN in autonomous networking, (2) improvements needed to integrate intents as fundamental policies for service modeling and network management, (3) need for appropriate representation models for intents in domain agnostic abstract manner, and (4) need to include learning as a fundamental function in autonomous networks. These observations provide the basis for in-depth investigation and standardization efforts for IBN as a fundamental network management paradigm in beyond 5G cellular networks.

Intent-based networking (IBN) provides a promising approach for managing networks and orchestrating services in beyond 5th Generation (B5G) deployments using modern service-based architectures. Public safety (PS) services form the basis of keeping society functional, owing to the responsiveness and avail-ability throughout the network. The provisioning of these services requires efficient and agile network management techniques with low-overhead and embedded intelligence. IBN incorporates the service subscribers in a model-driven approach to provision different user-centric services. However, it requires domain-specific and contextual processing of intents for abstracted management of network functions. This work proposes an intent definition for PS services in B5G networks, as well as a processing and orchestration architecture for a push-to-talk (PTT) use case. The simulation results show that PTT services adhere to the key performance indicators of access time and mouth-to-ear latency bounded by approximately 250 and 150 milliseconds, respectively, with an additional overhead experienced during the intent processing in the range of 20-40 milliseconds. This validates the premise of IBN in providing flexible and scalable management and service orchestration solution for PS next generation networks.

Research on autonomous management for large-scale deployments of constrained devices is still a maturing field in the Internet of Things (IoT). Although much research has been conducted on how to achieve autonomous management in specific cases, there is a need for literature investigating which mechanisms can achieve such behavior in a generalized way. In this review, we present a comprehensive and structured study of the mechanisms for autonomous device management of constrained IoT devices in the light of management tasks, operational environment, network topology, resource constraints, scalability and management categories. Data extracted from 32 relevant cases are first organized and analyzed according to a synthesized taxonomy of observed adaptation mechanisms, and then combined with the state-of-the-art models of autonomous operations, identifying common patterns for autonomous management. Based on our findings we substantiate best practices for designing and implementing solutions around adaptation mechanisms. We then present a generalized model for autonomous device management that describes and explains the processes required for autonomous operation, unifying the insights from previous works as one cohesive archetype.

For sustainable Internet-of-Things (IoT) systems, the solar power prediction is an essential element to optimize performance, allowing devices to schedule energy-intensive tasks in periods with excess energy. In regions with volatile weather, this requires taking the weather forecast into account. The problem is how to provide such solar energy predictions with high accuracy for large-scale IoT systems with various devices in an autonomous way, without manual adaptation effort. We present a detailed study on machine-learning approaches for the prediction of solar power intake for large-scale IoT systems. We examine which machine learning models, feature sets, and sampling rates gain the best results for a medium-term forecasting horizon. We also explore an operational setting in which devices are deployed without prior data and machine learning models are retrained for each sensor continuously as a form of online learning. Our results show that prediction errors can be reduced by 20 % compared to the state of the art, despite strong weather volatility.

The dissemination of digital devices across the seas and oceans of the world is becoming a reality, laying the foundations for sophisticated operations such as autonomous shipping or exposed fish-farming. This reflects the importance of maritime regions as well as the growing demand for more data and knowledge in these remote areas. Currently, the presence of information and communication technologies (ICTs) in offshore settings is limited by the lack of infrastructures, with communications being typically decoupled from the standard Internet or relying on expensive satellite solutions. This paper proposes integrating a commonly available maritime radio-technology, namely very high frequency (VHF) communications, with protocols used in the Internet of Things (IoT). By using the Internet Protocol version 6 (IPv6) over a low-power wireless personal area networks and the constrained application protocol (CoAP), interoperability is ensured between already existing maritime systems and the Internet. An experimental setup is evaluated under different settings and using different configurations. In addition, an analytical assessment of the solutions’ reliability is presented. The obtained results prove the feasibility of using IoT protocols over a VHF link and demonstrate that such an approach outperforms IPv4-based solutions. The conducted assessment reveals that, in constrained settings, configurations at the application layer (e.g., CoAP’s block size in Block-wise transfers) strongly impact the overall performance and reliability of the system. This motivates the development of new maritime solutions based on IPv6 for ensuring a sustainable and ubiquitous development of ICTs even in the most remote locations.

In this paper, we propose an OpenFlow enabled Internet infrastructure, using virtual path slicing in an end-to-end path, so that any user connected to an OpenFlow network is dynamically allocated a corresponding right of way. This approach allows an interference-free path, from other traffic, between any two endpoints, on multiple autonomous systems, for a given application flow (e.g., WebHD Video Streaming). Additionally, we propose and implement an end-to-end quality of service framework for the Future Internet and extend the virtual path slice engine to support future Internet technologies such as OpenFlow. The proposed framework is evaluated in distinct multiple autonomous scenarios for a city with a population of 1 million inhabitants, emulating xDSL (Digital Subscriber Line), LTE (Long-Term Evolution) and Fibre networking scenarios. The obtained results confirm the suitability of the proposed architecture between multiple autonomous systems, considering both data and control traffic scalability, as well as resilience and failure recovery. Furthermore, challenges and solutions for experimentation in a large-scale testbed are described.

The location of data centres is crucial when mobile network operators are moving towards cloudified mobile networks to optimize resource utilization and to improve performance of services. Quality of Experience (QoE) can be enhanced in terms of content access latency, by placing user content at locations where they will be present in the future. The Follow-Me Cloud (FMC) concept aims at optimising operations of moving Mobile Network Operators Services towards cloudified environments, where Information Centric Networking (ICN) and the appropriate content migration policies are of paramount importance. However, several factors need to be considered, including user movements and mobility prediction (MP), content popularity, and migration. This paper addresses all these aspects by implementing a fully integrated multi-criteria FMC and mobility prediction mechanisms (MP-FMC) on a cloud infrastructure. Experimental evaluation shows that MP-FMC can be orchestrated on-demand within a reasonable time frame, and it could deliver ≈ 33% improvement of content retrieval time.

With the increasing usage of mobile devices for traffic-heavy applications, mobile operators struggle for delivering good performance while, at the same time, optimizing resource consumption. By resorting to Information-Centric Networking and mobile cloud computing paradigms, mobile operators are able to tackle this challenge, simultaneously supporting more users and reducing operational costs. In this paper we propose a Mobile Follow-Me Cloud (M-FMC) model for enhancing the migration of content caches located at the edge of cloudified mobile networks, leveraging the benefits of such paradigms. This is achieved by accounting for content popularity and user mobility, optimizing systems' caches according to user interests and improving resource management. Evaluation results demonstrate that M-FMC model achieves more than 90% accuracy in real-time selection of content objects to be migrated between caches. This performance is reflected in higher cache-hit rates, therefore representing an improvement in content-access latency and overall bandwidth savings.

Infrastructure-less wireless multi-hop networks have long been proposed for natural disaster and warfare scenarios. However, the current demand of such networks has been towards social networking, gaming and ultimately, ubiquitous computing. In fact, the increasing number of users that own wireless capable devices is taking these networks to an entirely different scale. Existing routing protocols do not scale and do not consider the context wherein services operate. By presenting an alternative routing protocol that appropriately handles mobility of users among different contexts, large-scale clustered wireless networks are designed, using an efficient gateway selection with load-balancing capabilities. This protocol is based on the PhD work that defines a routing approach which uses a virtual hierarchy of clusters to explore the contextual-proximity of nodes, while reducing the total overhead of routing traffic even when compared with other cluster-based approaches. Moreover, this protocol also takes advantage of a Kernel-based link quality estimator, also considered during the course of the PhD thesis, which is capable of predicting gateway link disconnections, increasing the total amount of delivered data. The obtained results reveal that this routing scheme outperforms existing routing protocols regardless of the mobility pattern being used, being consistently lighter in overhead and delivering up to 50% more data traffic. These results motivate a new era of large-scale wireless multi-hop networks suitable for hand-held devices exchanging data amongst themselves.

Nowadays, the existing myriad of wireless capable devices has led to the development of numerous multi-hop routing protocols. From proactive to reactive and even hybrid routing approaches, these protocols have motivated the definition of autonomous and ubiquitous ad-hoc networks. Such networks have been idealized not only for disaster and rural scenarios, but also for an increasingly demanding social context in urban areas. However, being able to handle these networks in a large scale, still remains a challenge. Even though several routing solutions resort to clustering and hierarchies in order to limit routing information, the existing nodes' interactions are typically disregarded and mobility amongst different clusters still raises routing issues. In this work the scalability of three routing protocols will be analysed, by defining different size scenarios, while also assessing their routing performance with a mobile node moving between different clusters. Theoretical and simulation based results are presented, using twenty different possible transitions through the existing clusters. This evaluation provides an important contribution, revealing that hierarchical routing organizations' scalability is closer to what is theoretically expected, contrary to other routing solutions. Moreover, regarding the best routing performance which takes into account communities, these results motivate the further utilization of such schemes for future large scale ubiquitous networks.

Nowadays, the existing myriad of wireless capable devices has led to the development of numerous multi-hop routing protocols. In particular, due to the scale of these networks, routing protocols with well defined hierarchies have been proposed. By defining different size scenarios with twenty different possible transitions in hierarchies for 2, 3 and 4 clusters, the impact of using such mechanism is evaluated. Simulation based results are presented for 2 different routing protocols showing that a Deferred Routing approach is able to successfully handle hierarchical transitions, delivering up to 4.5 times more data traffic than the remaining protocols, while maintaining a lower overhead. These results motivate the further utilization of such scheme for future large scale wireless networks.

Ubiquitous broadband Internet access is an important requirement to satisfy user demands and support a new set of real time services and applications. IEEE 802.16, as a broadband wireless access technology, can significantly contribute in this area. It is able to provide high throughput over large distances, with intrinsic QoS support, in line-of-Sight and Non- line-of-Sight environments. This paper discusses a network architecture, based on IEEE 802.16 d compliant equipments, which is able to support real-time services with QoS integration through dynamic configuration of the 802.16 channel, using the NSIS framework. Tests have been carried out to evaluate the QoS performance of the defined architecture, demonstrating that it is perfectly capable of handling and supporting real time services, using dynamic configuration of the 802.16 channel, without introducing an excessive cost in the network.