Interdisciplinary Centre for Security, Reliability and Trust

The Internet has led to the creation of a digital society, where (almost) everything is connected and is accessible from anywhere. However, despite their widespread adoption, traditional IP networks are complex and very hard to manage. It is both difficult to configure the network according to predefined policies, and to reconfigure it to respond to faults, load, and changes. To make matters even more difficult, current networks are also vertically integrated: the control and data planes are bundled together. Software-defined networking (SDN) is an emerging paradigm that promises to change this state of affairs, by breaking vertical integration, separating the network's control logic from the underlying routers and switches, promoting (logical) centralization of network control, and introducing the ability to program the network. The separation of concerns, introduced between the definition of network policies, their implementation in switching hardware, and the forwarding of traffic, is key to the desired flexibility: by breaking the network control problem into tractable pieces, SDN makes it easier to create and introduce new abstractions in networking, simplifying network management and facilitating network evolution. In this paper, we present a comprehensive survey on SDN. We start by introducing the motivation for SDN, explain its main concepts and how it differs from traditional networking, its roots, and the standardization activities regarding this novel paradigm. Next, we present the key building blocks of an SDN infrastructure using a bottom-up, layered approach. We provide an in-depth analysis of the hardware infrastructure, southbound and northbound application programming interfaces (APIs), network virtualization layers, network operating systems (SDN controllers), network programming languages, and network applications. We also look at cross-layer problems such as debugging and troubleshooting. In an effort to anticipate the future evolution of this new paradigm, we discuss the main ongoing research efforts and challenges of SDN. In particular, we address the design of switches and control platforms - with a focus on aspects such as resiliency, scalability, performance, security, and dependability - as well as new opportunities for carrier transport networks and cloud providers. Last but not least, we analyze the position of SDN as a key enabler of a software-defined environment.

Satellite communications (SatComs) have recently entered a period of renewed interest motivated by technological advances and nurtured through private investment and ventures. The present survey aims at capturing the state of the art in SatComs, while highlighting the most promising open research topics. Firstly, the main innovation drivers are motivated, such as new constellation types, on-board processing capabilities, non-terrestrial networks and space-based data collection/processing. Secondly, the most promising applications are described, i.e., 5G integration, space communications, Earth observation, aeronautical and maritime tracking and communication. Subsequently, an in-depth literature review is provided across five axes: i) system aspects, ii) air interface, iii) medium access, iv) networking, v) testbeds & prototyping. Finally, a number of future challenges and the respective open research topics are described.

Initial efforts on wireless power transfer (WPT) have concentrated toward long-distance transmission and high power applications. Nonetheless, the lower achievable transmission efficiency and potential health concerns arising due to high power applications, have caused limitations in their further developments. Due to tremendous energy consumption growth with ever-increasing connected devices, alternative wireless information and power transfer techniques have been important not only for theoretical research but also for the operational costs saving and for the sustainable growth of wireless communications. In this regard, radio frequency energy harvesting (RF-EH) for a wireless communications system presents a new paradigm that allows wireless nodes to recharge their batteries from the RF signals instead of fixed power grids and the traditional energy sources. In this approach, the RF energy is harvested from ambient electromagnetic sources or from the sources that directionally transmit RF energy for EH purposes. Notable research activities and major advances have occurred over the last decade in this direction. Thus, this paper provides a comprehensive survey of the state-of-art techniques, based on advances and open issues presented by simultaneous wireless information and power transfer (SWIPT) and WPT assisted technologies. More specifically, in contrast to the existing works, this paper identifies and provides a detailed description of various potential emerging technologies for the fifth generation communications with SWIPT/WPT. Moreover, we provide some interesting research challenges and recommendations with the objective of stimulating future research in this emerging domain.

The next wave of wireless technologies is proliferating in connecting things among themselves as well as to humans. In the era of the Internet of Things (IoT), billions of sensors, machines, vehicles, drones, and robots will be connected, making the world around us smarter. The IoT will encompass devices that must wirelessly communicate a diverse set of data gathered from the environment for myriad new applications. The ultimate goal is to extract insights from this data and develop solutions that improve quality of life and generate new revenue. Providing large-scale, long-lasting, reliable, and near real-time connectivity is the major challenge in enabling a smart connected world. This paper provides a comprehensive survey on existing and emerging communication solutions for serving IoT applications in the context of cellular, wide-area, as well as non-terrestrial networks. Specifically, wireless technology enhancements for providing IoT access in the fifth-generation (5G) and beyond cellular networks, and communication networks over the unlicensed spectrum are presented. Aligned with the main key performance indicators of 5G and beyond 5G networks, we investigate solutions and standards that enable energy efficiency, reliability, low latency, and scalability (connection density) of current and future IoT networks. The solutions include grant-free access and channel coding for short-packet communications, non-orthogonal multiple access, and on-device intelligence. Further, a vision of new paradigm shifts in communication networks in the 2030s is provided, and the integration of the associated new technologies like artificial intelligence, non-terrestrial networks, and new spectra is elaborated. In particular, the potential of using emerging deep learning and federated learning techniques for enhancing the efficiency and security of IoT communication are discussed, and their promises and challenges are introduced. Finally, future research directions toward beyond 5G IoT networks are pointed out.

Abstract This paper presents an overview on the state of the art of semantics for abstract argumentation, covering both some of the most influential literature proposals and some general issues concerning semantics definition and evaluation. As to the former point, the paper reviews Dung's original notions of complete, grounded, preferred, and stable semantics, as well as subsequently proposed notions like semi-stable, ideal, stage, and CF2 semantics, considering both the extension-based and the labelling-based approaches with respect to their definitions. As to the latter point, the paper presents an extensive set of general properties for semantics evaluation and analyzes the notions of argument justification and skepticism. The final part of the paper is focused on the discussion of some relationships between semantics properties and domain-specific requirements.

Low earth orbit (LEO) satellite communications are expected to be incorporated in future wireless networks, in particular 5G and beyond networks, to provide global wireless access with enhanced data rates. Massive multiple-input multiple-output (MIMO) techniques, though widely used in terrestrial communication systems, have not been applied to LEO satellite communication systems. In this paper, we propose a massive MIMO transmission scheme with full frequency reuse (FFR) for LEO satellite communication systems and exploit statistical channel state information (sCSI) to address the difficulty of obtaining instantaneous CSI (iCSI) at the transmitter. We first establish the massive MIMO channel model for LEO satellite communications and simplify the transmission designs via performing Doppler and delay compensations at user terminals (UTs). Then, we develop the low-complexity sCSI based downlink (DL) precoder and uplink (UL) receiver in closed-form, aiming to maximize the average signal-to-leakage-plus-noise ratio (ASLNR) and the average signal-to-interference-plus-noise ratio (ASINR), respectively. It is shown that the DL ASLNRs and UL ASINRs of all UTs reach their upper bounds under some channel condition. Motivated by this, we propose a space angle based user grouping (SAUG) algorithm to schedule the served UTs into different groups, where each group of UTs use the same time and frequency resource. The proposed algorithm is asymptotically optimal in the sense that the lower and upper bounds of the achievable rate coincide when the number of satellite antennas or UT groups is sufficiently large. Numerical results demonstrate that the proposed massive MIMO transmission scheme with FFR significantly enhances the data rate of LEO satellite communication systems. Notably, the proposed sCSI based precoder and receiver achieve the similar performance with the iCSI based ones that are often infeasible in practice.

What will the future of UAV cellular communications be? In this tutorial article, we address such a compelling yet difficult question by embarking on a journey from 5G to 6G and expounding a large number of case studies supported by original results. We start by overviewing the status quo on UAV communications from an industrial standpoint, providing fresh updates from the 3GPP and detailing new 5G NR features in support of aerial devices. We then dissect the potential and the limitations of such features. In particular, we demonstrate how sub-6 GHz massive MIMO can successfully tackle cell selection and interference challenges, we showcase encouraging mmWave coverage evaluations in both urban and suburban/rural settings, and we examine the peculiarities of direct device-todevice communications in the sky. Moving on, we sneak a peek at next-generation UAV communications, listing some of the use cases envisioned for the 2030s. We identify the most promising 6G enablers for UAV communication, those expected to take the performance and reliability to the next level. For each of these disruptive new paradigms (non-terrestrial networks, cellfree architectures, artificial intelligence, reconfigurable intelligent surfaces, and THz communications), we gauge the prospective benefits for UAVs and discuss the main technological hurdles that stand in the way. All along, we distil our numerous findings into essential takeaways, and we identify key open problems worthy of further study.

This paper studies secrecy rate optimization in a wireless network with a single-antenna source, a multi-antenna destination and a multi-antenna eavesdropper. This is an unfavorable scenario for secrecy performance as the system is interference-limited. In the literature, assuming that the receiver operates in half duplex (HD) mode, the aforementioned problem has been addressed via use of cooperating nodes who act as jammers to confound the eavesdropper. This paper investigates an alternative solution, which assumes the availability of a full duplex (FD) receiver. In particular, while receiving data, the receiver transmits jamming noise to degrade the eavesdropper channel. The proposed self-protection scheme eliminates the need for external helpers and provides system robustness. For the case in which global channel state information is available, we aim to design the optimal jamming covariance matrix that maximizes the secrecy rate and mitigates loop interference associated with the FD operation. We consider both fixed and optimal linear receiver design at the destination, and show that the optimal jamming covariance matrix is rank-1, and can be found via an efficient 1-D search. For the case in which only statistical information on the eavesdropper channel is available, the optimal power allocation is studied in terms of ergodic and outage secrecy rates. Simulation results verify the analysis and demonstrate substantial performance gain over conventional HD operation at the destination.

We propose a data-aided transmit beamforming scheme for the multi-user multiple-input-single-output (MISO) downlink channel. While conventional beamforming schemes aim at the minimization of the transmit power subject to suppressing interference to guarantee quality of service (QoS) constraints, here we use the knowledge of both data and channel state information (CSI) at the transmitter to exploit, rather than suppress, constructive interference. More specifically, we design a new precoding scheme for the MISO downlink that minimizes the transmit power for generic phase shift keying (PSK) modulated signals. The proposed precoder reduces the transmit power compared to conventional schemes, by adapting the QoS constraints to accommodate constructive interference as a source of useful signal power. By exploiting the power of constructively interfering symbols, the proposed scheme achieves the required QoS at lower transmit power. We extend this concept to the signal to interference plus noise ratio (SINR) balancing problem, where higher SINR values compared to the conventional SINR balancing optimization are achieved for given transmit power budgets. In addition, we derive equivalent virtual multicast formulations for both optimizations, both of which provide insights of the optimal solution and facilitate the design of a more efficient solver. Finally, we propose a robust beamforming technique to deal with imperfect CSI, that also reduces the transmit power over conventional techniques, while guaranteeing the required QoS. Our simulation and analysis show significant power savings for small scale MISO downlink channels with the proposed data-aided optimization compared to conventional beamforming optimization.

Satellite communication systems are a promising solution to extend and complement terrestrial networks in unserved or under-served areas, as reflected by recent commercial and standardization endeavors. In particular, 3GPP recently initiated a study item for new radio, i.e., 5G, non-terrestrial networks aimed at deploying satellite systems either as a stand-alone solution or as an integration to terrestrial networks in mobile broadband and machine-type communication scenarios. However, typical satellite channel impairments, as large path losses, delays, and Doppler shifts, pose severe challenges to the realization of a satellite-based NR network. In this paper, based on the architecture options currently being discussed in the standardization fora, we discuss and assess the impact of the satellite channel characteristics on the physical and medium access control layers, both in terms of transmitted waveforms and procedures for enhanced mobile broadband and narrowband-Internet of Things applications. The proposed analysis shows that the main technical challenges are related to the PHY/MAC procedures, in particular random access, timing advance, and hybrid automatic repeat request and depending on the considered service and architecture, different solutions are proposed.

Multibeam satellite systems have been employed to provide interactive broadband services to geographical areas under-served by terrestrial infrastructure. In this context, this paper studies joint multiuser linear precoding design in the forward link of fixed multibeam satellite systems. We provide a generic optimization framework for linear precoding design to handle any objective functions of data rate with general linear and nonlinear power constraints. To achieve this, an iterative algorithm which optimizes the precoding vectors and power allocation alternatingly is proposed and most importantly, the proposed algorithm is proved to always converge. The proposed optimization algorithm is also applicable to nonlinear dirty paper coding. As a special case, a more efficient algorithm is devised to find the optimal solution to the problem of maximizing the proportional fairness among served users. In addition, the aforementioned problems and algorithms are extended to the case that each terminal has multiple co-polarization or dual-polarization antennas. Simulation results demonstrate substantial performance improvement of the proposed schemes over conventional multibeam satellite systems, zero-forcing and regularized zero-forcing precoding schemes in terms of meeting the traffic demand, e.g., using real beam patterns, over twice higher throughput can be achieved compared with the conventional scheme. The performance of the proposed linear precoding scheme is also shown to be very close to the dirty paper coding.

The next phase of satellite technology is being characterized by a new evolution in non-geostationary orbit (NGSO) satellites, which conveys exciting new communication capabilities to provide non-terrestrial connectivity solutions and to support a wide range of digital technologies from various industries. NGSO communication systems are known for a number of key features such as lower propagation delay, smaller size, and lower signal losses in comparison to the conventional geostationary orbit (GSO) satellites, which can potentially enable latency-critical applications to be provided through satellites. NGSO promises a substantial boost in communication speed and energy efficiency, and thus, tackling the main inhibiting factors of commercializing GSO satellites for broader utilization. The promised improvements of NGSO systems have motivated this paper to provide a comprehensive survey of the state-of-the-art NGSO research focusing on the communication prospects, including physical layer and radio access technologies along with the networking aspects and the overall system features and architectures. Beyond this, there are still many NGSO deployment challenges to be addressed to ensure seamless integration not only with GSO systems but also with terrestrial networks. These unprecedented challenges are also discussed in this paper, including coexistence with GSO systems in terms of spectrum access and regulatory issues, satellite constellation and architecture designs, resource management problems, and user equipment requirements. Finally, we outline a set of innovative research directions and new opportunities for future NGSO research.

Low earth orbit (LEO) satellite communications are expected to be incorporated in future wireless networks, in particular 5G and beyond networks, to provide global wireless access with enhanced data rates. Massive multiple-input multiple-output (MIMO) techniques, though widely used in terrestrial communication systems, have not been applied to LEO satellite communication systems. In this paper, we propose a massive MIMO transmission scheme with full frequency reuse (FFR) for LEO satellite communication systems and exploit statistical channel state information (sCSI) to address the difficulty of obtaining instantaneous CSI (iCSI) at the transmitter. We first establish the massive MIMO channel model for LEO satellite communications and simplify the transmission designs via performing Doppler and delay compensations at user terminals (UTs). Then, we develop the low-complexity sCSI based downlink (DL) precoder and uplink (UL) receiver in closed-form, aiming to maximize the average signal-to-leakage-plus-noise ratio (ASLNR) and the average signal-to-interference-plus-noise ratio (ASINR), respectively. It is shown that the DL ASLNRs and UL ASINRs of all UTs reach their upper bounds under some channel condition. Motivated by this, we propose a space angle based user grouping (SAUG) algorithm to schedule the served UTs into different groups, where each group of UTs use the same time and frequency resource. The proposed algorithm is asymptotically optimal in the sense that the lower and upper bounds of the achievable rate coincide when the number of satellite antennas or UT groups is sufficiently large. Numerical results demonstrate that the proposed massive MIMO transmission scheme with FFR significantly enhances the data rate of LEO satellite communication systems. Notably, the proposed sCSI based precoder and receiver achieve the similar performance with the iCSI based ones that are often infeasible in practice.

Today's smartphones and mobile devices typically embed advanced motion sensors. Due to their increasing market penetration, there is a potential for the development of distributed sensing platforms. In particular, over the last few years there has been an increasing interest in monitoring vehicles and driving data, aiming to identify risky driving maneuvers and to improve driver efficiency. Such a driver profiling system can be useful in fleet management, insurance premium adjustment, fuel consumption optimization or CO2 emission reduction. In this paper, we analyze how smartphone sensors can be used to identify driving maneuvers and propose SenseFleet, a driver profile platform that is able to detect risky driving events independently from the mobile device and vehicle. A fuzzy system is used to compute a score for the different drivers using real-time context information like route topology or weather conditions. To validate our platform, we present an evaluation study considering multiple drivers along a predefined path. The results show that our platform is able to accurately detect risky driving events and provide a representative score for each individual driver.

This paper deals with day-ahead security management with respect to a postulated set of contingencies, while taking into account uncertainties about the next day generation/load scenario. In order to help the system operator in decision making under uncertainty, we aim at ranking these contingencies into four clusters according to the type of control actions needed to cover the worst uncertainty pattern of each contingency with respect to branch overload. To this end we use a fixed point algorithm that loops over two main modules: a discrete bi-level program (BLV) that computes the worst-case scenario, and a special kind of security constrained optimal power flow (SCOPF) which computes optimal preventive/corrective actions to cover the worst-case. We rely on a DC grid model, as the large number of binary variables, the large size of the problem, and the stringent computational requirements preclude the use of existing mixed integer nonlinear programming (MINLP) solvers. Consequently we solve the SCOPF using a mixed integer linear programming (MILP) solver while the BLV is decomposed into a series of MILPs. We provide numerical results with our approach on a very large European system model with 9241 buses and 5126 contingencies.

Different research communities varying from telecommunication to traffic engineering are working on problems related to vehicular traffic congestion, intelligent transportation systems, and mobility patterns using information collected from a variety of sensors. To test the solutions, the first step is to use a vehicular traffic simulator with an appropriate scenario in order to reproduce realistic mobility patterns. Many mobility simulators are available, and the choice is usually done based on the size and type of simulation required, but a common problem is to find a realistic traffic scenario. In order to evaluate and compare new communication protocols for vehicular networks, it is necessary to use a wireless network simulator in combination with a vehicular traffic simulator. This additional step introduces further requirements for the scenario. The aim of this work is to provide a scenario able to meet all the common requirements in terms of size, realism and duration, in order to have a common basis for the evaluations. In the interest of building a realistic scenario, we decided to start from a real city with a standard topology common in mid-size European cities, and real information concerning traffic demands and mobility patterns. In this paper we show the process used to build the Luxembourg SUMO Traffic (LuST) Scenario, and present a summary of its characteristics together with an overview of its possible use cases.

The present work focuses on the forward link of a broadband multibeam satellite system that aggressively reuses the user link frequency resources. Two fundamental practical challenges, namely the need to frame multiple users per transmission and the per-antenna transmit power limitations, are addressed. To this end, the so-called frame-based precoding problem is optimally solved using the principles of physical layer multicasting to multiple co-channel groups under per-antenna constraints. In this context, a novel optimization problem that aims at maximizing the system sum rate under individual power constraints is proposed. Added to that, the formulation is further extended to include availability constraints. As a result, the high gains of the sum rate optimal design are traded off to satisfy the stringent availability requirements of satellite systems. Moreover, the throughput maximization with a granular spectral efficiency versus SINR function, is formulated and solved. Finally, a multicast-aware user scheduling policy, based on the channel state information, is developed. Thus, substantial multiuser diversity gains are gleaned. Numerical results over a realistic simulation environment exhibit as much as 30% gains over conventional systems, even for 7 users per frame, without modifying the framing structure of legacy communication standards.

This letter presents the first work introducing a deep learning (DL) framework for channel estimation in large intelligent surface (LIS) assisted massive MIMO (multiple-input multiple-output) systems. A twin convolutional neural network (CNN) architecture is designed and it is fed with the received pilot signals to estimate both direct and cascaded channels. In a multi-user scenario, each user has access to the CNN to estimate its own channel. The performance of the proposed DL approach is evaluated and compared with state-of-the-art DL-based techniques and its superior performance is demonstrated.

Despite the growth of prevention techniques, phishing remains an important threat since the principal countermeasures in use are still based on reactive URL blacklisting. This technique is inefficient due to the short lifetime of phishing Web sites, making recent approaches relying on real-time or proactive phishing URL detection techniques more appropriate. In this paper, we introduce PhishStorm, an automated phishing detection system that can analyze in real time any URL in order to identify potential phishing sites. PhishStorm can interface with any email server or HTTP proxy. We argue that phishing URLs usually have few relationships between the part of the URL that must be registered (low-level domain) and the remaining part of the URL (upper-level domain, path, query). We show in this paper that experimental evidence supports this observation and can be used to detect phishing sites. For this purpose, we define the new concept of intra-URL relatedness and evaluate it using features extracted from words that compose a URL based on query data from Google and Yahoo search engines. These features are then used in machine-learning-based classification to detect phishing URLs from a real dataset. Our technique is assessed on 96 018 phishing and legitimate URLs that result in a correct classification rate of 94.91% with only 1.44% false positives. An extension for a URL phishingness rating system exhibiting high confidence rate ( $>$ 99%) is proposed. We discuss in this paper efficient implementation patterns that allow real-time analytics using Big Data architectures such as STORM and advanced data structures based on the Bloom filter.

As exposed in a recent report by General Electric, an industrial Internet of Things (IoT) is emerging as a commercially viable embodiment of the IoT where physical sensors gather data readings from the field and deliver the traffic to the Internet. The collected real-time big data, in turn, allow the optimizing of entire industry verticals with enormous return of investments. Although opportunities are ample, it comes along with serious engineering design challenges as industrial applications have stringent requirements on delay, lifetime and standards-compliance. To this end, we advocate the use of an IEEE/IETF standardized IoT architecture along with a recently introduced data-centric scheduling algorithm known as traffic aware scheduling algorithm (TASA). Applying graph theoretical tools to the multi-channel, time-synchronized, and duty-cycled nature of TASA, we rigorously derive optimality and bounds on the minimum number of needed active slots (impacting end-to-end delays) and the network duty-cycle (impacting lifetime). We demonstrate the enormous superiority of TASA over traditional IEEE802.15.4/ZigBee approaches in terms of energy efficiency. The outcome of this paper is currently to lay foundations of the recently formed IETF standardization group 6TSCH with the aim to significantly improve IoT data flows over IEEE802.15.4e TSCH and IETF 6LoWPAN/ROLL enabled technologies.