The advent of 5G and Beyond 5G networks has propelled the development of innovative applications and services that harness network programmability, data from management and control interfaces, and the capabilities of network slicing. However, ensuring these applications function as intended and effectively utilize 5G/B5G capabilities remains a challenge, mainly due to their reliance on complex interactions with control plane Network Functions. This work addresses this issue by proposing a novel architecture to enhance the onboarding, orchestration, and validation of 5G/B5G-capable applications and services, while enabling the creation of application-tailored network slices. By integrating DevOps principles into the NFV ecosystem, the proposed architecture automates workflows for deployment, testing, and validation, while adhering to standardized onboarding models and continuous integration practices. Furthermore, we also address the realization of such architecture into a platform that supports extensive testing across multiple dimensions, including 5G readiness, security, performance, scalability, and availability. Besides introducing such a platform, this work also demonstrates its feasibility through the orchestration and validation of an automotive application that manages virtual On-Board Units within a 5G-enabled environment. The obtained results underscore the effectiveness of the proposed architecture, as well as the performance and scalability of the platform that materializes it. By integrating DevOps principles, our work aids in reducing deployment complexity, automating testing and validation, and enhancing the reliability of next-generation Network Applications, therefore accelerating their time-to-market.

The rapid growth of mobile network traffic and the emergence of complex applications, such as self-driving cars and augmented reality, demand ultra-low latency, high throughput, and massive device connectivity, which traditional network design approaches struggle to meet. These issues were initially addressed in 5th Generation (5G) and Beyond-5G (B5G) networks, where Artificial Intelligence (AI), particularly Deep Learning (DL), is proposed to optimize the network and to meet these demanding requirements. However, the resource constraints and time limitations inherent in telecommunication networks raise questions about the practicality of deploying large Deep Neural Networks (DNNs) in these contexts. This paper analyzes the costs of implementing DNNs by comparing them with shallow ML models across multiple datasets and evaluating factors such as execution time and model interpretability. Our findings demonstrate that shallow ML models offer comparable performance to DNNs, with significantly reduced training and inference times, achieving up to 90% acceleration. Moreover, shallow models are more interpretable, as explainability metrics struggle to agree on feature importance values even for high-performing DNNs.

Artificial intelligence (AI) is a fundamental pillar in developing next-generation networks. Federated learning (FL) emerges as a promising solution to address data privacy concerns during AI model training within the network. However, training AI models on user equipment raises challenges regarding battery consumption, unreliable connections, and communication overhead. This paper proposes Zenoh, a data-centric communication middleware, as an alternative to the traditional Message Passing Interface (MPI) for FL applications. Zenoh’s decentralized nature and low communication overhead make it suitable for resource-constrained devices and unreliable network connections. The paper compares Zenoh and MPI in a realistic FL scenario, demonstrating Zenoh’s potential to outperform MPI in terms of flexibility, communication efficiency, and system complexity.

The growth of Machine Learning (ML) has led to the commercialization of applications like data analytics, autonomous systems, and security diagnostics. These models are becoming widespread across various domains. However, security and privacy issues accompany this growth. Although actively researched, there's fragmentation in analyzing and defining ML models' resilience. This work examines the resilience of shallow ML models against typical data poisoning attacks. Our study assessed their strengths in adversarial scenarios using the MNIST dataset in a CAPTCHA context. Results show notable resilience, with accuracy and generalization maintained despite malicious inputs, offering insights to strengthen future ML systems. Understanding the mechanisms enabling this resilience can aid in fortifying the security of future ML systems.

Serverless computing is becoming increasingly relevant in the cloud and applied in other fields, such as telecom. This paper proposes the Moving Functions as a Service (MoFaaS) system, a Moving Target Defense (MTD) and N-Version Programming (NVP)-based approach designed to improve the protection of applications and services built using Function as a Service (FaaS). The mechanism works by rotating the version to be executed next for each function. Therefore, when an attacker triggers the execution of a specific workflow path in distinct moments, the versions of the functions responding to those requests will probably be different. Consequently, if one or more function variants are vulnerable, the attacker cannot target them reliably. In the end, we conducted a preliminary practical demonstration to prove this system’s effectiveness. Its analysis shows that the attack difficulty increases with the number of versions per function and the versions an attacker has to compromise to achieve its objectives.

The lack of testing and validation mechanisms for 5G Network Applications poses a severe challenge in ensuring their correct behavior and in reducing their time to market. This work addresses the intricate complexities of validating such Applications and defines some research questions related to this research field. These questions focus on problems that are still unresolved, and based on them we establish future directions for solving such intricate research problems.

NFV has risen to be a solution to abstract Network Functions from the hardware, providing numerous advantages to Network Operators. However, many challenges have appeared with the evolution of NFV. An example are the inter-domain scenarios where services are spanned across multiple independent domains. Using VPNs to interconnect all domains can attenuate the difficulties imposed by such scenarios. In this paper, we present a NFV-based solution for deploying full-mesh VPNs to interconnect different administrative domains, without manual intervention.

Standardization organizations, such as the European Telecommunications Standards Institute (ETSI), have been gathering efforts to specify the Edge Computing paradigm. However, there is still a lack of complete, interface-wise, actual implementations and evaluations of a fully functional Edge Computing architecture. On these grounds, the work presented in this paper proposes a new Multi-access Edge Computing (MEC)-Network Functions Virtualization (NFV) architecture for a challenging Business to Business to Consumer (B2B2C) model, based on the references provided by ETSI, and provides a prototype implementation to demonstrate its viability. The tests conducted show that the proposed framework can be efficiently deployed, allowing Telecommunications Operators to rapidly instantiate and provide an elastic Edge Infrastructure to their customers.

Our society is a complex network of people that requires cooperation and socialization to thrive, being that interaction and communication between individuals have been key parts of our society since the beginning of its existence. With the rise of the Covid-19 pandemic and the need to quarantine, this complex need has been truly affected on many sectors, and we became less connected. Technology was an important factor during the early days of the pandemic, with people starting to opt for certain systems and platforms that allowed them to stay in touch with each other, however nowadays, there are still massive limitations to these platforms when it comes to an interaction’s ambiance due to the lack of user immersion. In this paper, we present an MMORPG with a scalable infrastructure able to host events for hundreds of people, but most importantly that allows users to communicate in a more realistic manner with a set of functionalities that fulfil the goal to combat the undesirable isolation. To verify the effectiveness of our platform, questionnaires, and usability tests were conducted.

5G technologies provide several advancements regarding low latency and high bandwidth network scenarios, thus enabling new vertical use cases. Nonetheless, the lack of testing and validation mechanisms for NFV-based services poses a severe challenge in reducing their time to market. In this work we showcase a service to automate the validation of 5G NetApps, thus striving towards reducing the time to market of these applications, and introducing a new layer of trust in their reliability and availability.

Data collection within a real-world environment may be compromised by several factors such as data-logger malfunctions and communication errors, during which no data is collected. As a consequence, appropriate tools are required to handle the missing values when analysing and processing such data. This problem is often tackled via matrix decomposition. While it has been successfully applied in a wide range of applications, in this work we report an issue that has been neglected in literature and “degenerates” the quality of the imputations obtained by matrix decomposition in multivariate time-series (with smooth evolution). Briefly, the problem consists of the misalignment of the matrix decomposition result: the missing values imputations fall within an incorrect range of values and the transitions between observed and imputed values are not smooth. We address this problem by proposing a postprocessing alignment strategy. According to our experiments, the post-processing adjustment substantially improves the accuracy of the imputations (when the misalignment occurs). Moreover, the results also suggest that the misalignment occurs mostly when dealing with a small number of time-series due to lack of generalisation ability.

The fact that most IoT solutions are provided by third parties, along with the pervasiveness of the collected data, raises privacy and security concerns. There is a need to verify which data is being sent to the third party, as well as preventing those channels from becoming an exploitation avenue. We propose to use existing API definition languages to create contracts which define the data that can be transmitted, their format and constraints. To verify the compliance with these contracts, we propose a Network Service architecture which validates REST-like API requests/responses against a Swagger schema. We deal with encrypted traffic using an Service Function Chaining (SFC)-enabled Man-in-the-Middle (MITM), allowing verifications in “real-time.” We devised a Proof of Concept and showed that we were able to detect (and stop) contract violations.

In this paper, we propose an architecture based on NFV and SDN which allows to balance traffic analysis techniques using a Classifier. It steers flows to the appropriate Service Function Chaining (to open traffic or not) according to network requirements (such as, effectiveness, flexibility, scalability, performance, and privacy). The SSL/TLS traffic processing is carried-out by the centerpiece of this work, the SFC-enabled MITM. A Proof-of-Concept was conducted (focusing on our SFC-enabled MITM) which showed that functionalities lost due to encryption (Content Optimization, Caching, Network Anti-virus, and Content Filter) were recovered when processing opened traffic within its Service Function Chains. We also evaluated its impact on performance. The results show that cipher suite overhead plays a role but can be mitigated, the Classifier can alleviate the performance overhead of different traffic analysis techniques, network functions have lower impact to performance, and Service Function Chaining length influences page load time.

The increasing number of small, cheap devices full of sensing capabilities lead to an untapped source of information that can be explored to improve and optimize several systems. Yet, as this number grows it becomes increasingly difficult to manage and organize all this new information. The lack of a standard context representation scheme is one of the main difficulties in this research area (Antunes et al., 2016b). With this in mind we propose a stream characterization model which aims to provide the foundations of a new stream similarity metric. Complementing previous work on context organization, we aim to provide an automatic organizational model without enforcing specific representations.

As the number of environmental sensors grows, it becomes increasingly difficult to manage, store and process all these sources of information. Several context representation schemes try to standardize this information, however none of them have been widely adopted. Instead of proposing yet another context representation scheme, we discuss efficient ways to deal with this diversity of representation schemes. We defined the basic requirements for flexible context storage systems, proposed an implementation and compared our implementation against two other approaches. Our solution provides more value than the remaining solutions without suffering a significant decrease in performance.

Social networks have recently emerged to become vital tools for information and content dissemination among connections. Indeed, the immense increase of the number of users of Facebook made it rise to become the largest existing social network with more than 1.2 billion active users. However, these numbers also rose the attention of hackers and attackers who aim at propagating malware and viruses for obtaining confidential information regarding social network users. In this manner, it is crucial that each Facebook user is able to easily access, control and analyse the information shared on the corresponding profile so that profile usage deviations can be more efficiently detected. However, despite the fact that Facebook allows an analysis of all user actions through the Timeline Review, this information is not comprehensively organized and there is no statistical analysis of the user generated data. In this paper, we propose a novel framework comprising a Facebook event collector, which by being provided with an authentication token for a user profile obtained through a Facebook application developed for this purpose, collects all the corresponding posted information and stores it in a relational database for a posteriori analysis. Through the graphical interface of the developed application, users can access all stored information in a comprehensible manner, according to the type of event, thus facilitating the analysis of the user's behaviour. By storing each event with the corresponding timestamp, we are able to perform an efficient and comprehensive analysis of all posted contents and compute statistical models over the obtained data. In this manner, we can create a notion of normal usage profile and detect possible deviations which may be indicative of a compromised user account.

The proliferation of user-owned connected devices has brought value to mobile application developers, which can make use of locally-available sensors and capabilities and send their information to the web, centralizing the data flows. A more distributed approach would have device capabilities offered directly on the network as services hosted by the user. These pervasive user-hosted services could be made discoverable and available over a public federated service infrastructure. The infrastructure would provide transport over an identity layer, where endpoints are addressed by their identities instead of network identifiers, and on top of which services can be exposed to be consumed by trusted friends or anonymous users, as the hosting user prefers. The work presented in this paper explores the possibility of implementing such a distributed social Service-Oriented Architecture (SOA) over Extensible Messaging and Presence Protocol (XMPP). This SOA, which would expose re-usable coarse-grained software components in a service ecosystem, differs from traditional SOA because it attempts to counter the centralization existing services, in favour of a fully-distributed service ecosystem where each peer can behave both as service consumer and provider. Finally, an analysis is done on how suitable XMPP is to serve as a base protocol for such infrastructure.

The utilization of Media Independent Handover (MIH) mechanisms, such as the ones provided by the IEEE802.21 standard, allow the abstraction of control and information querying primitives of different wireless access technologies, using a common interface. This not only simplifies the design of high-level entities interfacing with said technologies, but by providing a transport protocol for that interface, such primitives can be used to extend controlling mechanisms towards remote entities. However, the standard only employs such flexible and abstraction capabilities towards handover optimization and facilitation scenarios. In this paper, we take advantage of the flexible design of the MIH mechanisms, and propose evolutions over their base design, allowing their integration into the areas of Internet Multimedia Optimization and the Internet of Things.

As personal information and context sharing applications gain traction more attention is drawn to the associated privacy issues. These applications address privacy using an unsatisfactory “whitelist” approach, similar to social networks “friends”. Some of them also link location publishing with user interaction which is also a form of privacy control - the user has to explicitly say where he is. There are a few automatic location based-services (LBS) that track the user, but without more adequate privacy protection mechanisms they enable even bigger threats to the user. On previous work, an XMPP-based Context Distribution Architecture was defined, more suitable for the distribution of frequently changing context than other systems because it is based on the publish-subscribe pattern. In this paper the authors present an extension to this architecture that allows for the introduction of a complex degree of access control in context distribution. The devised changes enable the system to consider a number of interesting context privacy settings for context distribution control. Also, this control must be enforced in a way that it doesn't interfere with the real-time nature of the distribution process. After describing the enhancements to the architecture, a prototype of the system is presented. Finally, the delivery latency and additional processing introduced by the access control components is estimated by testing it against the existing system.

The wide deployment of Wireless Local Area Networks (WLAN) we are witnessing today increases connectivity opportunities for mobile terminal devices, such as smartphones. However, continuous scanning for WLAN points of attachment can be a power exhausting mechanism for such battery-powered devices. These mobile devices, besides being equipped with different wireless access interfaces, are also coupled with sensors such as accelerometer, GPS, luminance and magnetic compass. In fact, sensors are increasingly being coupled into different devices and environments and are able to convey sensing information through networks into decision entities able to optimize different processes. In this paper we propose a framework where media independent sensing information is used to enhance wireless link management towards energy-efficiency. This framework enables the dissemination of sensing information towards local and remote decision entities, enhancing other processes (e.g. mobility) with sensing information in order to provide true Ambient Intelligence scenarios. We introduce this framework into a wireless management scenario able to provide energy-efficient optimal network connectivity.

Multimedia group communication over IP is an advanced service gaining growing interest from broadcast, fixed and mobile network operators. Next Generation Networks (NGN) will require that IP based multimedia services to be efficiently delivered to groups over heterogeneous networks with QoS. Although being acknowledged as the most efficient way of delivering group communication in packet switched networks, IP multicast still faces some problems associated with QoS that have limited its success in the past. Furthermore the nonexistence of true multicast in wireless technologies has led to an inefficient mapping between the IP and wireless layers. This paper proposes a technique for efficient dynamic mapping of IP Multicast, optimizing its usage for NGN. This technique is evaluated by a prototype on a WiFi environment.

The wireless digital lifestyle comes to the expense of less privacy and security. This environment is prone to be monitored by rogue users, eager to learn from our lifestyle habits and use them for their own profit. The IP protocol provides very few mechanisms, in order to safeguard user privacy and impair efficient data-mining of user habits. This paper will address an identity architecture that makes use of both data (L2) and network (L3) layer identifiers in order to provide a pseudonimization function, based on virtual hoarding concepts. Our proposal is especially interesting when able to exploit the broadcast and promiscuous nature of wireless communications that usually is regarded as a security concern. A prototype implementation has been developed and tested.

This paper describes the architecture and current capabilities of Carl, a prototype of an intelligent service robot, designed having in mind such tasks as serving food in a reception or acting as a host in an organization. The approach that has been followed in the design of Carl is based on an explicit concern with the integration of the major dimensions of intelligence, namely communication, action, reasoning and learning. The paper focuses on the multi-modal human-robot communication capabilities of Carl, since these have been significantly improved during the last year.

The development of robots capable of accepting instructions in terms of familiar concepts to the user is still a challenge. For these robots to emerge it’s essential the development of natural language interfaces, since this is regarded as the only interface acceptable for a machine which expected to have a high level of interactivity with Man. Our group has been involved for several years in the development of a mobile intelligent robot, named Carl, designed having in mind such tasks as serving food in a reception or acting as a host in an organization. The approach that has been followed in the design of Carl is based on an ex-plicit concern with the integration of the major dimensions of intelligence, namely Communication, Action, Reasoning and Learning. This paper focuses on the multi-modal human-robot language communication capabilities of Carl, since these have been significantly improved during the last year. 1.