To facilitate digital convergence, 6G mobile technologies are designed with significantly improved performance indicators. In fact, Quality-of-Service (QoS) increases between one and two magnitude orders compared to previous 5G networks. This extreme QoS must be managed in two different planes. Horizontally, every 6G local station must ensure all connected devices are provided with the required QoS, while, vertically, all devices operating a common application must perceive a stable network performance regardless of their geographical position. Traditionally, teletraffic theory reduces the complexity of user populations using concepts such as “busy hour” or “maximum demand”. But in ultra-high-density scenarios, this approach would cause resource underutilization and economic failure. For most authors, the response to this challenge is a new network architecture where intelligent models being able to discover hidden patterns and relations could be seamlessly integrated. Despite their numerical success, intelligence models still face some critical open questions. First, complex deep learning models require exhaustive datasets in which all behaviors are homogenously represented and described by a large enough number of entries. Second, pure optimization algorithms can reach operation points that, while optimum, are not acceptable in legal, contractual, social, or technological terms. In this paper, we propose a network-informed learning architecture, where intelligent algorithms and models are supervised, evaluated, and corrected through network and social laws in feedback loops, can ensure an effective extremely reliable QoS in 6G ultra-high-density scenarios. Local models are later consolidated vertically and horizontally, allowing an optimum and network-acceptable operation, even when non-exhaustive datasets are used from training or when unexpected biases are introduced. Experimental simulations are used to evaluate the improvement and performance of the proposed solution. Results show QoS objectives are met with a 99.999

In this paper, we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks. This system enables end nodes to select the optimum time and scheme to transmit private data safely. In 6G dynamic heterogeneous infrastructures, unstable links and non-uniform hardware capabilities create critical issues regarding security and privacy. Traditional protocols are often too computationally heavy to allow 6G services to achieve their expected Quality-of-Service (QoS). As the transport network is built of ad hoc nodes, there is no guarantee about their trustworthiness or behavior, and transversal functionalities are delegated to the extreme nodes. However, while security can be guaranteed in extreme-to-extreme solutions, privacy cannot, as all intermediate nodes still have to handle the data packets they are transporting. Besides, traditional schemes for private anonymous ad hoc communications are vulnerable against modern intelligent attacks based on learning models. The proposed scheme fulfills this gap. Findings show the probability of a successful intelligent attack reduces by up to 65% compared to ad hoc networks with no privacy protection strategy when used the proposed technology. While congestion probability can remain below 0.001%, as required in 6G services.

P2P networks and distributed systems have become the key enabling technologies for the most innovative services and applications, such as Blockchain or the dark web. Actually, P2P architectures show relevant advantages such as reliability, transparency, or accountability. Furthermore, traditional intruders and malware can infect and affect the global behavior of these networks with a very low probability (because of the large number of participating nodes). However, distributed systems are forced to communicate and transfer data between P2P nodes. And those data are vulnerable, as they can be captured while being sent. Nowadays, intelligent attacks are especially dangerous and worrying, as illegitimate learning models capture private information to generate new knowledge and discover patterns. In standard protection mechanisms, encryption techniques are used to prevent access to data, but two main open questions arise. First, powerful new models can learn from encrypted data and, second, some information is always public and vulnerable, such as the Internet addresses. Therefore, new protection technologies for P2P networks are needed. In this paper, we propose a solution based on false synthetic information injection. The vulnerability and state of P2P nodes is described using a dynamical system, which analyzes how nodes change their exposure with time, so at any moment it is possible to estimate the percentage of exposed data and communication flows. With this estimation, a computational model based on elementary functions and describing the learning level of potential (and unknown) intelligent attackers is run, so it is possible to deduct the amount of false information to be injected, and intelligent attackers get confused, and their learning level reduces. An experimental validation supported by simulation scenarios is provided as well, and results prove the proposed scheme reduces the attacking success rate by up to 21

The upcoming data economy is fully supported by the fact that data are valuable. In this context, issues such as data property or data sovereignty become relevant for citizens and governments. Different regulations such as the General Data Protection Regulation in the European Union, or the Data Act Political Agreement, are in charge of preserving the rights of data owners (typically citizens) at legal level. But, in many cases, laws describe or recognize rights that cannot be provided from a technological or engineered point of view. In fact, in most digital services, users must give their data to service providers and they have no option. As, in general, service architectures consider a unique centralized data repository owned by the provider. This data transfer implies an inevitable loss of data sovereignty because there is no mechanism available for users to execute its governance. In this context, innovative service architectures are needed, in order to facilitate a distributed and effective data sovereignty to citizens. Therefore, in this paper, we present a novel Blockchain-enabled architecture for chat applications, enabling users to exercise a real data governance. The application connects users through a Blockchain network where Smart Contracts manage contacts and message exchanges in three different kinds of chats: individual public chats, group public chats, and secret chats. Data are encrypted using a combination of symmetric and asymmetric techniques and keys, so data owners can control access to their messages and information dynamically and without transferring them to any external repository. An experimental validation is provided using a pilot deployment. Results show that the architecture is scalable and provides an acceptable Quality-of-Experience to potential users.

Future 6G networks are expected to meet extreme Quality-of-Service (QoS) requirements. From delays below one hundred microseconds to bitrates above ten gigabit per second. Besides, these requirements must be guaranteed in massive scenarios, with densities above ten million devices per square kilometer, and be compatible with intense mobility where speed can reach up to one thousand kilometers per hour. In this context, network resource management cannot be static. Any fixed network configuration meeting such extreme requirements should be designed for the worst case, and then it would be oversized and non-profitable. To facilitate a tailored and efficient network resource distribution, dynamic management techniques are needed; so 6G nodes can envision the upcoming needs and get adapted to provide the expected QoS. However, previously reported predictive models for network resource management are either exclusively focused on software instances and service availability, or they consider network resources are a static pool to be optimally distributed among devices and/or verticals. New dynamic management schemes are required, centered on devices and their specific characteristics (high density, mobility, privacy restrictions, etc.), as well as they can predict the number of needed resources to serve the upcoming demand. This paper fills this gap. We propose a predictive management algorithm which can calculate the probability of network congestion for a given amount of resources, using the traffic theory and Gaussian models. Those models are generated through a federated scheme, where base stations periodically monitor the devices' resource consumption and produce a partial model with additive noise to preserve the devices' privacy. The network core collects all partial models and uses clustering technologies to produce a time-variant global model describing the dynamic resource demand. Scheduling policies are implemented to ensure the management algorithms do not have a relevant impact on network operations. An experimental validation based on simulation tools is also provided. Results show the achieved prediction precision is close to 93%, and the network resource consumption reduces up to 26% compared to a static configuration.

6G networks are envisioned to provide an extremely high quality-of-service (QoS). Then, future 6G network must operate in the Ka-band, where more bandwidth and radio channels are available, and noise and interferences are lower. But even in this context, 6G base stations must adjust the transmission power to ensure the signal-to-noise ratio is good enough to enable the expected QoS. However, 6G networks are not the only infrastructure operating in that band. Actually, many scientific instruments are also working on those frequencies. Considering that 6G networks will be transmitting a relevant power level, they can interfere very easily with these scientific instruments. Therefore, in this paper we propose a new solution to enable the interferenceless coexistence between 6G networks and scientific instruments. This solution includes a three-dimensional model to analyse future positions of user devices. Using this information and an interference model, we design a decision model to adapt the transmitted power, so the QoS achieves the expected level. Besides, when the transmitted power is high enough to interfere with close scientific instruments, a scheduling algorithm based on swarm intelligence is triggered. This algorithm calculates the optimum distribution of time slots and radio channels, so the scientific instruments can operate, and the 6G networks can still provide the required QoS. An experimental validation is provided to analyse the performance of the proposed solution. Results show a complete coexistence may be achieved with an interference level of -26 dBm and a QoS above 95% of the expected level.

The Industry 4.0 revolution is characterized by distributed infrastructures where data must be continuously communicated between hardware nodes and cloud servers. Specific lightweight cryptosystems are needed to protect those links, as the hardware node tends to be resource-constrained. Then Pseudo Random Number Generators are employed to produce random keys, whose final behavior depends on the initial seed. To guarantee good mathematical behavior, most key generators need an unpredictable voltage signal as input. However, physical signals evolve slowly and have a significant autocorrelation, so they do not have enough entropy to support high-randomness seeds. Then, electronic mechanisms to generate those high-entropy signals artificially are required. This paper proposes a robust hyperchaotic circuit to obtain such unpredictable electric signals. The circuit is based on a hyperchaotic dynamic system, showing a large catalog of structures, four different secret parameters, and producing four high entropy voltage signals. Synchronization schemes for the correct secret key calculation and distribution among all remote communicating modules are also analyzed and discussed. Security risks and intruder and attacker models for the proposed solution are explored, too. An experimental validation based on circuit simulations and a real hardware implementation is provided. The results show that the random properties of PRNG improved by up to 11% when seeds were calculated through the proposed circuit.

To build robust secure channels for information exchange, distributed computing systems must generate and handle high-entropy secret keys. However, solutions to generate those high-entropy keys such as Physical Unclonable Functions or sensing devices are very dependent on the environment and the hardware performance. Thus, keys may not achieve the expected entropy or show uncontrolled behaviors that may prevent communicating remote nodes to synchronize with a shared key. Therefore, new solutions are needed to enable distributed computing nodes to generate high-entropy keys in a lightweight, consistent, and robust manner. In this paper we propose a federated algorithm to address this challenge. Remote nodes are provided with different physical devices to initialize with a random configuration a Fibonacci random number generator. The parameter set describing the configuration of the key generator is locally encoded using a gradient function and sent to an edge computing manager where different encoded configurations coming from different remote nodes are collected. The edge computing manager combines all these configurations considering different weights and an optimization target function based on the definition of mutual information. An experimental validation is also provided. Simulation tools are employed, and results show the long-term average entropy increases up to 23% when using the proposed solution.

Molecular communications are envisioned to transform medicine and environmental sciences, but currently only small, isolated networks have been deployed. It is essential, then, to develop new mechanisms to enable information flow from remote users to nanometric biological machines through next-generation networks, such as 6G mobile technologies. 6G mobile networks are characterized by an extreme Quality-of-Service, but in the context of molecular communications, two requirements turn critical: ultra-massive and extremely reliable communications. Molecular communications are simplex, so there is typically no channel to transmit acknowledgment messages. Furthermore, several nanometric receptors concentrated in just some square micrometers are an ultra-massive device density for 6G base stations. In this paper, we propose a computational algorithm to make reliable and massive 6G mobile molecular communications feasible. The proposed algorithm employs clustering to create a real-time map with the positions of the biological nanometric machines, and particle swarm optimization to track the identity of the different machines while slowly moving. To handle ultra-massive density, clustering operates by defining which magnetic particles used as communication interface belong to the same biological machine. While the optimization mechanism considers the current and previous clustering results and a probabilistic model to determine the identity of each cell. Reliability is achieved by an acknowledgment message generated by a 6G transceiver when the biological machines reach the expected destination. Simulation tools are employed to validate the proposed solution. Results show that the identification error is less than 15

Future 6G communications are envisioned to enable a large catalogue of pioneering applications. These will range from networked Cyber-Physical Systems to edge computing devices, establishing real-time feedback control loops critical for managing Industry 5.0 deployments, digital agriculture systems, and essential infrastructures. The provision of extensive machine-type communications through 6G will render many of these innovative systems autonomous and unsupervised. While full automation will enhance industrial efficiency significantly, it concurrently introduces new cyber risks and vulnerabilities. In particular, unattended systems are highly susceptible to trust issues: malicious nodes and false information can be easily introduced into control loops. Additionally, Denialof-Service attacks can be executed by inundating the network with valueless noise. Current anomaly detection schemes require the entire transformation of the control software to integrate new steps and can only mitigate anomalies that conform to predefined mathematical models. Solutions based on an exhaustive data collection to detect anomalies are precise but extremely slow. Standard models, with their limited understanding of mobile networks, can achieve precision rates no higher than 75%. Therefore, more general and transversal protection mechanisms are needed to detect malicious behaviors transparently. This paper introduces a probabilistic trust model and control algorithm designed to address this gap. The model determines the probability of any node to be trustworthy. Communication channels are pruned for those nodes whose probability is below a given threshold. The trust control algorithm comprises three primary phases, which feed the model with three different probabilities, which are weighted and combined. Initially, anomalous nodes are identified using Gaussian mixture models and clustering technologies. Next, traffic patterns are studied using digital Bessel functions and the functional scalar product. Finally, the information coherence and content are analyzed. The noise content and abnormal information sequences are detected using a Volterra filter and a bank of Finite Impulse Response filters. An experimental validation based on simulation tools and environments was carried out. Results show the proposed solution can successfully detect up to 92% of malicious data injection attacks.

Instrumentation systems are essential in many critical applications such as air defense and natural disaster prediction and control. In these systems, the Quality-of-Service, measurement capacity, resilience, and efficiency are higher than in traditional monolithic instruments, thus ultra-reliable low latency, broadband, and massive communications are required to communicate all those machines. In such scenario, 5G communication technologies are seen as a promising solution. To achieve that, 5G networks provide a new radio interface in which hundreds of antennas are used to serve around tens of users in each frequency slot. This approach is only feasible if all those antennas are controlled by a hybrid transmission and reception chain, where radio beams are conformed. However, this approach opens the door to innovative cyber-physical attacks. For instance, digital and analog beamforming algorithms may be poisoned to spread the energy in the free space, deny the 5G communication services, and use that as a vector to attack and degrade the instrumentation systems. In this paper, we describe a new method for poisoning 5G beamforming algorithms based on passive radio-obstacles. Our mathematical framework allows an attacker to manage an obstacle made of unit cells of absorbent materials and varactors, so it can mix and reflect MIMO radio signals in such a way beamforming algorithms get confused and spread all the energy into free space. To validate the proposed approach, a simulation scenario was built, where different beamforming algorithms were considered. Results show the proposed attack is successful with all kinds of hybrid and analog beamforming algorithms, so more than 90% of the available power is spread in the free space and Quality-of-Service of instrumentation systems is degraded around 77%.

Empowering users through the provision of more and enriched information about the (food) products they buy, so that they are able to make more conscious decisions, is one of the key objectives of the European Commission and the European framework programmes for research and innovation. The Industry 4.0 revolution has made it technologically possible to achieve this objective, but some logistic challenges remain open. Mainly, the great complexity of current supply chains makes it very difficult to “move” information from primary producers to final customers. A global agreement about data formats, information storage, confidentiality, etc. is nowadays unreachable. Thus, innovative tools that do not require such coordination and that allow, although partial, a relevant data sharing policy are needed. Different experiments have been reported, but, in general, they are focused on unelaborated products (such as vegetables or chicken meat). Solutions for more complex products, such as bakery products, which are composed of several different ingredients need to be investigated. This paper addresses this gap. In this paper, we describe a new tool for elaborated products traceability (named TrFood) based on QR codes, web technologies, and composition schemes. This application may also control the ecological footprint and the geographical origin of the supplies and final products. Users may obtain all this information using a specific mobile application. Furthermore, real deployment and experimental validation with real users was carried out in the context of the European DEMETER project. Results show a relevant improvement in the Quality-of-Experience of customers when using the TrFood tool.

Cyber-Physical Systems are very vulnerable to sparse sensor attacks. But current protection mechanisms employ linear and deterministic models which cannot detect attacks precisely. Therefore, in this paper, we propose a new non-linear generalized model to describe Cyber-Physical Systems. This model includes unknown multivariable discrete and continuous-time functions and different multiplicative noises to represent the evolution of physical processes and random effects in the physical and computational worlds. Besides, the digitalization stage in hardware devices is represented too. Attackers and most critical sparse sensor attacks are described through a stochastic process. The reconstruction and protection mechanisms are based on a weighted stochastic model. Error probability in data samples is estimated through different indicators commonly employed in non-linear dynamics (such as the Fourier transform, first-return maps, or the probability density function). A decision algorithm calculates the final reconstructed value considering the previous error probability. An experimental validation based on simulation tools and real deployments is also carried out. Both, the new technology performance and scalability are studied. Results prove that the proposed solution protects Cyber-Physical Systems against up to 92% of attacks and perturbations, with a computational delay below 2.5 s. The proposed model shows a linear complexity, as recursive or iterative structures are not employed, just algebraic and probabilistic functions. In conclusion, the new model and reconstruction mechanism can protect successfully Cyber-Physical Systems against sparse sensor attacks, even in dense or pervasive deployments and scenarios.

Traditional solutions for bridging the digital divide focus on providing last-mile connectivity solutions (backhaul and mid-haul) for underserved populations such as villages and rural environments. In this paper, we propose a novel solution for improving the quality of local communications in those environments by leveraging architectural aspects of 5G communications networks. This idea, which we call TaguaSpot, was created especially for local settlements in tropical countries like the Republic of Panama. The TaguaSpot Radio Access Network (RAN) design architecture is versatile and has the functionality needed to deliver digital communications services in most environmental constraints of these indigenous communities. TaguaSpot is based on low-cost, low-energy hotspots with Edge Computing capabilities as well as other 5G network principles in order to enhance the local communication experience for users in remote locations.

Ambient Intelligence deployments are very vulnerable to Cyber-Physical attacks. In these attacking strategies, intruders try to manipulate the behavior of the global system by affecting some key elements within the deployment. Typically, attackers inject false information, integrate malicious devices within the deployment, or infect communications among sensor nodes, among other possibilities. To protect Ambient Intelligence deployments against these attacks, complex data analysis algorithms are usually employed in the cloud to remove anomalous information from historical series. However, this approach presents two main problems. First, it requires all Ambient Intelligence systems to be networked and connected to the cloud. But most new applications for Ambient Intelligence are supported by isolated systems. And second, they are computationally heavy and not compatible with new decentralized architectures. Therefore, in this paper we propose a new decentralized security solution, based on a Blockchain ledger, to protect isolated Ambient Intelligence deployments. In this ledger, new sensing data are considered transactions that must be validated by edge managers, which operate a Blockchain network. This validation is based on reputation metrics evaluated by sensor nodes using historical network data and identity parameters. Through information theory, the coherence of all transactions with the behavior of the historical deployment is also analyzed and considered in the validation algorithm. The relevance of edge managers in the Blockchain network is also weighted considering the knowledge they have about the deployment. An experimental validation, supported by simulation tools and scenarios, is also described. Results show that up to 93% of Cyber-Physical attacks are correctly detected and stopped, with a maximum delay of 37 s.

In the last fifteen years communication paradigms have radically changed. From relations mostly based on synchronous and facetoface conversations, to formal emails and phone calls and, today, videoconferences and different asynchronous chat mobile applications. This change has also affected higher education, where many innovative strategies and instructional tools enabling communication among students and with professors, have been implemented. The final objective of all these mechanisms is to increase the impact of the tutorial action, thanks to a fast and continuous interaction with professors and the collaborative learning among students. However, informal observations seem to show that some tools and/or strategies are more successful than others, depending on the teaching and learning methodology. Therefore, this paper aims to study the impact of different communication tools and strategies in the higher education students' learning, including academic results, motivation competence acquisition level. We are focusing on blended and online methodologies, as they are implemented in most current engineering degrees. The study considers five subjects analyzed during three different courses. Four different communication instructional tools were also studied, including forums, emails, Telegram and Discord. Besides, four different communication strategies were also considered. Results were evaluated using statistical methods. Results show the improvement in the students' learning is especially relevant when chat applications and immediate responses are provided in the context of online teaching methodologies. In blended methodologies, on the other hand, chat applications are clearly preferred too, although improvement may be achieved with almost any tool.

In the last ten years, many different innovative learning methodologies have been proposed for engineering education. In those new approaches, students learning, and evaluation are usually supported by a catalogue of creative activities, practices, and presentations which may be very numerous if competencies to be acquired are heterogeneous and must cover a large knowledge area, such as in cybersecurity courses. In addition, when a high number of students are enrolled in the course, deadlines must be strict, to enable professors to evaluate all students and activities properly and on time. Due to this requiring schedule, students often cannot work all competencies with the expected depth, and their learning decreases. New instruments are needed to facilitate the learning of the students and the evaluation process are needed. Therefore, in this paper we proposed a new automated scheduling tool, based on graph theory, to fill this gap. The tool employs graph coloring algorithms to calculate all possible schedules for evaluation activities. Students may freely choose the schedule that best fits their personal situation, learning progress, background, etc. using a web portal where all solutions from the coloring algorithm are displayed. This tool was deployed in the Computer Engineering degree at Universidad Politécnica de Madrid, Spain. A pilot experience was conducted for three years (2020-2022) in the context of a cybersecurity course to analyze how this new tool improves the learning and the evaluation process of students. Results show both academic results and the students’ and professor’ satisfaction improve in a significant manner.

DOES THE EXAM FORMAT INFLUENCE THE STUDENTS’ ACADEMIC RESULTS? MULTIPLE-CHOICE EXAMS VS SHORT-ANSWER TYPE EXAMS IN ENGINEERING COURSES