The exponential growth in connectivity and computing demand has made Network Function Virtualization Infrastructure (NFVI) a major contributor to global energy consumption. Conventional network and service deployments, whether based on legacy hardware appliances or NFVI stacks, struggle to dynamically provision resources for peak data traffic demand. This results in nearly constant energy consumption, even during low-traffic periods, leading to inefficient resource use. Network softwarization and virtualization have enabled flexible and programmable service deployments, which are beneficial for the rapid and dynamic scaling of network functions. This paper validates energy-aware service and network orchestration with a Zero-touch Network and Service Management (ZSM) framework for the autonomous optimization of computing, network, and power resources from NFVI in a use case focused on connected mobility, and in particular, smart traffic management. By modeling road traffic based on vehicle count and type and 3rd Generation Partnership Project (3GPP) profiles for data formats in vehicular communication scenarios, the ZSM framework adjusts services and resources to service requirements and to actual demand. Experimental validation on the real-life Smart Highway testbed in Antwerp, Belgium, demonstrates a strong correlation between vehicular traffic and power consumption, supporting the hypothesis that adaptive compute and network resource management reduces unnecessary energy use and advances the vision of sustainable and self-optimizing Sixth-Generation (6G) networks.

Despite major advances in Connected and Autonomous Vehicles (CAVs), edge cases such as unmapped construction zones or dense urban areas still require human intervention. Teleoperation, often combined with Level 4 automation, addresses these situations but demands strict network performance i.e., latency below 5 ms, uplink throughput above 25 Mbps, and 99.999% reliability, to ensure safe, responsive control under heavy load. This paper presents a Proof-of-Concept (PoC) 5G Standalone (SA) network supporting end-to-end Network Slicing for teleoperation. The PoC achieves seamless slice isolation across the 5G Core (5GC), Transport Network (TN), and Radio Access Network (RAN), enabling dynamic and cross-domain Network Slicing to meet teleoperation requirements.

This demonstration paper presents a real-life 5G Standalone proof-of-concept showcasing end-to-end network slicing with both inter-slice and intra-slice isolation across the 5G Core, Transport Network, and Radio Access Network domains. The system, based on a smart hospital scenario, highlights a decentralized 5G architecture that aligns slice selection, controlplane, and data-plane mechanisms to guarantee that network resources are securely partitioned across the entire infrastructure. The demonstration highlights the technical implementation of slice orchestration and performance isolation, showing how critical services such as robotic-surgery control or vital-sign sensors maintain guaranteed quality of service even under high load, while less critical services coexist without interference. The proof-of-concept confirms the practical viability of complete end-to-end network slicing for mission-critical applications that demand reliability, confidentiality, and predictable performance.

This paper presents a comprehensive benchmark study of a real-life 5G Standalone (SA) deployment with different Multiple Input Multiple Output (MIMO) configurations (1x1, 2x2, and 4x4) in an indoor office environment. We evaluate the impact of distance, obstacles, and material composition on key performance metrics, including throughput, Reference Signal Received Power (RSRP), Signal-to-Interference-plus-Noise Ratio (SINR), and Rank Indicator (RI). The results demonstrate that while higher-order MIMO configurations can deliver substantial throughput gains under favorable conditions, their effectiveness is fundamentally constrained by environmental factors such as signal attenuation, multipath propagation, and material-induced losses. Our results provide practical guidelines for indoor network planning and optimization, establishing concrete performance baseline for open-source 5G systems in typical office scenarios highlighting the critical importance of site selection.

The real-world deployments of 5G SA networks have highlighted significant challenges, particularly related to signal coverage, leading to performance degradation for enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and massive Machine-Type Communications (mMTC). To address these challenges and minimize the costs of new infrastructure deployment, Network Sharing among multiple operators has become a viable, cost-effective solution. The 3rd Generation Partnership Project (3GPP) began exploring network sharing in 5G with Release 15, expanding it with an Indirect Network Sharing configuration in Release 19. In this work, we present an Indirect Network Sharing approach that utilizes Network Slicing to create multiple isolated virtual networks on a single physical infrastructure, ensuring resource isolation and efficient management in a multi-operator environment. Our demonstration illustrates how a third-party entity can effectively manage network resources, maintaining isolation and performance quality across different network domains operated by various providers.

5G Standalone (SA) networks introduce a concept of Network Slicing that enables a range of new applications, such as enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and massive Machine-Type Communications (mMTC). However, despite the promising potential of 5G SA networks, real-world deployments have revealed significant limitations, particularly in terms of signal coverage, resulting in performance degradation for eMBB, URLLC, and mMTC services. To mitigate these challenges and reduce the costs associated with deploying new infrastructure, Network Sharing among multiple operators has emerged as a cost-effective solution. While the 3rd Generation Partnership Project (3GPP) introduced Network Sharing in 5G Release 15 and added an Indirect Network Sharing configuration in Release 19, real-life implementation remains limited due to immature mechanisms and the lack of automated systems for neutral hosts providers to easily onboard new operators and dynamically allocate network resources to meet specific network requirements. In this paper, we explore the application of Network Slicing as a mechanism to deploy Network Sharing among multiple operators, presenting a 5G SA Indirect Network Sharing architecture as proof of concept (PoC). Through our experiment, performed in a real-world and open-source testbed based on O-RAN principles, we demonstrate how applying Network Slicing technology, Neutral Host providers can effectively deploy resource isolation and enable collaboration in a multi-operator environment while guaranteeing service quality to their users.

The diversity of Beyond 5G (B5G)/Sixth-Generation (6G) technologies and the increasing density of Internet-of-Things (IoT) clients are pushing the traditional MANagement and Orchestration (MANO) into more complex scenarios. The European Telecommunications Standards Institute (ETSI) Industry Specification Group (ISG) Zero-touch Network and Service Management (ZSM) has published several documents that present ZSM as a visionary concept, which promises to deliver benefits for BSG and 6G networks, such as increased efficiency in MANO and for tackling the challenges and limitations endured in the transition from human-intervention assisted network management to fully autonomous network management. This work-in-progress paper provides insights into the recent trends in defining and developing ZSM, the efforts towards standardization of its guidelines, coverage of existing solutions that apply the ZSM principles, a discussion about the imperative of its adoption in the domains of the evolving mobile generations and a proposal for its application in a vehicular use case of Smart Traffic Management (STM), where B5G/6G applications are enhanced by a robust ZSM capability of edge resources, to improve the effectiveness of STM.

The paper demonstrates the VITAL-5G platform capabilities to improve the adoption and effectiveness of 5G and beyond solutions within the Transport & Logistic domain by bridging the knowledge gap between industry stakeholders, network experts, and service developers. Therefore, in this paper, we present four distinct capabilities the VITAL-5G platform offers i) Facilitating deployment of vertical service in the 5G network, ii) Real-time monitoring of network and service performance and iii) Advanced failure diagnostics. IV) Utilization of 5G slices. To enhance the adoption and effectiveness of 5G and beyond solutions within the T&L domain, we showcase how the VITAL-5G platform hides the operational complexity from the experimenters, allowing them to express their network, application, and hardware requirements in a human-readable format, while in turn deploying complex services on the 5G SA infrastructure.

In the rapidly evolving Industry 4.0 landscape, the integration of industrial robots and Artificial Intelligence (AI) is revolutionizing the processes involved in storing and managing goods. While these advancements hold the promise of enhancing operational efficiency, they necessitate a robust and high-performing indoor network infrastructure. This demo paper introduces a dynamic network slicing mechanism tailored for Wi-Fi networks, capitalizing on readily available Commercial Off-The-Shelf (COTS) devices, and seamlessly incorporating In-Band Network Telemetry (INT) within a Software Defined Networking (SDN) framework. To effectively navigate the intricacies and uncertainties of network environments, we employ Fuzzy Logic to oversee queueing disciplines (qdisc), which directly influence air-time—the duration a device allocates to transmitting or receiving data over a wireless channel. Through a series of experimental demonstrations, we highlight the effectiveness of our proposed mechanism in maintaining stringent Quality of Service (QoS) standards even in conditions of network saturation. Our solution guarantees uninterrupted and streamlined operations, even in high-demand scenarios

The DEDICAT-6G project emphasizes dynamic re-sourcing, connectivity, flexibility, and trustworthiness for emerging human-centric services. In response, our Vulnerable Road User safety solution targets the need for heightened awareness in urban settings, leveraging edge computing, communication protocols, and efficient resource management to reduce intersection-related accidents. Our architecture involves Road Side Units, On-Board Units, and Vulnerable Road Users, with a Collision Detection Edge Application for real-time data analysis, incorporating a Monitoring System that facilitates comprehensive performance evaluation. A simulation considering limited sight scenarios at intersections yielded promising early results, indicating the efficacy of our approach. Future endeavors encompass real-world implementation, advanced algorithm integration, resource optimization, and environmental impact assessment, all contributing to advancing secure and adaptive communication systems.

The design of video game interfaces for children with neurodevelopmental disorders requires a holistic and multidisciplinary analysis, considering the characteristics of the users towards whom they are directed, and the therapeutic use of technologies for early intervention and diagnosis of these disorders, as well as the design of visual and interactive elements to motivate and influence the behaviour of their final recipients. We proposed a system of requirements which is based on the analysis and review of video game design methodologies, the interface design process, neurodesign, emotional design and the different manifestations of neurodevelopmental disorders. Added to all of the above is the review of more than seventy video games aimed at children with neurodevelopmental disorders, which allowed us to know the regularities of this type of product. For validation, we run a workshop for educators on topics aimed at the design of digital applications for the diagnosis and intervention of patients with neurodevelopmental disorders, from which the design of two video games was derived, one aimed at language disorders and one working memory and another to attention disorder and hyperactivity. The participation of specialists allowed us to validate the requirements system and optimize the video game development process.

Enhancing communication between Vulnerable Road Users (VRUs) and Unmanned Automated Vehicles (UAVs) has significant potential to improve road safety. The need for this communication is due to the fact that VRUs will no longer be able to establish physical eye contact with UAVs, given the absence of a human driver behind the steering wheel. However, a challenge in the state-of-the-art technologies for Connected, Cooperative, and Automated Mobility (CCAM), i.e. ITS-G5 (IEEE 802.11p) and Cellular Vehicle-to-Everything (C-V2X), is the lack of a unified communication stack that connects all types of users. This is because the current generation of CCAM communication technologies requires dedicated hardware devices that cannot be easily installed on devices carried by VRUs (such as phones or wearables). This paper aims to address this challenge by providing a real-life, sophisticated solution that offers the CCAM communication stack as a Network-as-a-Service (NaaS) in the 5G and Beyond ecosystem. Integration is achieved by relying on the Service Enabler Architecture Layer (SEAL) principles standardised by the 3rd Generation Partnership Project (3GPP). These architectural principles are embedded in the design of Network-Aware Edge Applications (EdgeApps), which are the building blocks of vertical services in 5G and Beyond. This way, any device or user with the capability to connect to 5G will also be able to retrieve important CCAM services from the network by using EdgeApps. In addition, no dedicated CCAM hardware is needed. Furthermore, this paper provides key lessons learned from the challenges encountered in connecting VRUs and UAVs by integrating CCAM into the 5G and Beyond ecosystem. Moreover, we have conducted real-life experiments to evaluate the system-level latency characteristics of the proposed solution and compared them with those of ITS-G5 and C-V2X.

Connected and Automated Vehicles (CAVs) are revolutionizing the automotive industry by improving real-time situational awareness, and road safety. Connectivity and latency are critical for the secure and efficient operation of CAVs. The evolution of Cellular Vehicular-to-Everything (C-V2X) technology, particularly through Long Term Evolution V2X (LTE-V2X) and its successor New Radio-V2X (NR-V2X), is essential to address these challenges. LTE-V2X and NR-V2X are intended to coexist, complementing each other to cover a broad spectrum of vehicular communication needs. However, network overload is a critical issue, which risks severely degrading the performance of V2X applications and compromising road safety. This study delves into the practical implementation of Network Slicing within a real-world 5G environment, incorporating a modular Open Radio Access Network (O-RAN) architecture on the radio side, and Service-Based Architecture (SBA) principles on the core. We present a Network Slicing configuration that deploys a synergy between the 5G Core (5GC) and the Radio Access Network (RAN). Through strategic placement and policy application across multiple User Plane Functions (UPFs), our configuration enhances network performance and reliability for V2X applications. We validate our approach by demonstrating how this setup effectively manages the high demands of diverse and rigorous applications, ensuring the network requirements for enhanced V2X scenarios under various network conditions. Our results highlight the importance of synergy between 5GC and RAN for the application of an efficient network slicing mechanism in NR-V2X networks.

As 5G rollout keeps progressing, the need for experimentation platforms with different functionalities, like MEC deployments, flexible Core functions and multi-vendor compatibility, keeps growing. Existing testbeds have some commercial/closed source solutions integrated to a degree (in the Core, Radio Access Network (RAN) or both), limiting development of new and innovative functionalities, and experimentation that might require more fine grain control over the network. This paper provides valuable insights into the current 5G performance by using the 5GOpen@TheBeacon testbed, a flexible solution that utilizes multiple open-source solutions in both indoor and outdoor environments, working in real-world conditions. We leverage two architectures and combine the different software available which later can enable research across different use cases, such as eMBB, URLLC and V2X.

Recent advances in Vehicle-to-Everything (V2X) communication have significantly improved road safety. However, the requirements of future vehicular applications are rapidly surpassing the capabilities of current generation cellular networks. Future wireless networks are expected to use millimeter wave (mmWave) frequencies due to spectrum availability and promises of very high throughput and ultra-low latency. These frequencies suffer from high path loss and are sensitive to mobility, which poses new challenges for V2X. In this paper, we investigate vehicular communication at mmWave frequencies through extensive simulation. We design, implement, and simulate various vehicular scenarios to investigate the impact of mmWave frequencies and mobility on system performance, with a specific focus on teleoperated driving applications due to their high uplink throughput requirements. Our simulation results demonstrate the potential of mmWave networks for vehicular communication, while highlighting the need to address challenges such as limited uplink capacity and poor reliability.

As network management operations increasingly rely on automation and finer control actions, there is a need for precise telemetry systems. In-band Network Telemetry (INT) methods use data packets to carry telemetry and give real-time insights about network performance. Existing solutions often require specialized hardware or offer limited runtime configuration options. This work presents an INT Framework for heterogeneous private networks, targeting industrial and multimedia applications. The framework is designed to be flexible and runtime-reconfigurable, addressing challenges in real-world applications. We provide implementation details of our elements supporting the configurability and the consolidation of raw telemetry into high-level Quality of Service (QoS) metrics. We evaluated the framework in a testbed with wired and wireless devices. The results show the accuracy in monitoring QoS, as well as an analysis of synchronization requirements, showcasing the feasibility of our framework for solutions requiring precise and flexible QoS monitoring.

Vehicular communication is a core technology of Intelligent Transportation Systems (ITS). Vehicle-to-vehicle (V2V) communication still needs to develop resilience, such that communication is safe and efficient, in time-critical applications. The radio-based systems, such as cellular V2X (C-V2X) and Dedicated Short Range Communication (DSRC), which are used classically for vehicular communication suffer from performance degradation in traffic scenarios where traffic is dense. In recent years, Line of Sight (LoS) technologies such as Visible Light Communication (VLC) are considered complementary technology to Radio Frequency (RF). VLC utilizes the light-emitting diodes (LEDs) headlamps and tail lights that are standard on modern vehicles to exchange information with the predecessor and subsequent vehicle. This work-in-progress paper highlights the need to combine RF and LoS technologies to improve the stability and reliability of$V2 V$communication. Therefore, we discuss the different LoS and RF technologies, and we present the combinations that can be used for communication. Finally, we propose a hybrid strategy that combines the best properties of individual technologies.

The integration of various technologies in a common goal is carried out in different branches of science. The management of the heritage found in university museums is of vital importance for the culture of students, teaching and the persistence of the historical memory of the institution. The aim of this work is to propose a system that integrates the Internet of Things (IoT) and Augmented Reality (AR) that allows specialists to manage this collection more efficiently with the use of these technologies. To achieve this goal, interviews and data collection on the collection, analysis of the state of the art on the application of these technologies in museums and the possible expected results and the innovative nature of the project have been shown.

To properly orchestrate challenging services such as those deployed for Vehicle-to-Everything (V2X) use cases, MANO systems need to be intelligent and automated. Network Function Virtualization (NFV) and Machine Learning (ML) provide opportunities for automating MANO operations, and this paper presents our MI-enhAnced Edge Service orchesTRatiOn (MAESTRO) algorithm that makes proactive ML-driven decisions for edge service relocation to ensure Quality of Service (QoS) guarantees for V2X services. Moreover, to validate the effectiveness of our proposed solution, we have performed the experimentation using real-life testbeds for high computing and smart mobility i.e., Smart Highway and Virtual Wall, located in Antwerp and Gent, Belgium. The contribution of our paper is two-fold: i) we study the interrelation between the Key Performance Indicators (KPIs) measured at the vehicle client side, and the infrastructure metrics at the edge computing nodes and ii) we propose and evaluate an ML-based quality-aware algorithm that automates edge service orchestration to decrease average latency while guaranteeing high service availability and reliability.

The shipping sector has become one of the corner-stone aspects of modern production systems, which has been impacting economic growth over the past decades. Its digitalization is expected to make significant improvements in ship control safety and reliability by enabling autonomous operations. Nonetheless, there are still many challenges that need to be thoroughly studied, and in this paper, we focus on one of them, i.e., the communication between barges, ports, and services, as the increased network latency and the limitations on the bandwidth imposed by satellite communications could result in significant risks for accident occurrence. Thus, we present one of the first attempts to leverage the potential of 5G systems for automating barge operations, starting from teleoperation as an enabler of automation, toward creating and validating a cellular-based automated barge control system in a real-life environment.

Vehicular Edge Computing (VEC) brings cloud infrastructure to the vehicular edge, resulting in better performances and avoiding network congestions. In this work-in-progress paper, the benefits of edge computing over cloud computing are discussed in a vehicular environment context, and they are leveraged by creating a Cooperative, Connected and Automated Mobility (CCAM) performance measurement framework. This measurement tool can follow vehicles by moving across different devices, enabling measurements on Key Performance Indicators (KPIs) using edge computing. We already used this tool to evaluate latencies of both a stationary and driving vehicle, moving over the Smart Highway testbed in Antwerp, Belgium. When driving, smart-edge-following algorithms can be deployed to choose the nearest Road Side Unit (RSU) using broadcasted Cooperative Awareness Messages (CAMs) of the vehicle. While driving on the Smart Highway, the application monitors important performance metrics such as throughput, latency, packet loss, packet delivery rate and more. We compare short-range vehicular communications technologies on the Smart Highway (ITS-G5 and LTE-V2X PC5) against the cellular. Our preliminary results demonstrate the benefits in terms of latency by using short-range communications technologies in VEC applications. These results validate that moving applications to the edge is truly beneficial, since our results confirmed up to 90% lower latency using ITS-G5, up to 50% using LTE-V2X PC5. Future deployments of 5G in the Smart Highway are planned, which would further improve the performance edge computing technologies.

University heritage includes all the results of the academic, research, and extension activities of the university community, bequeathed in values, knowledge, and modes of action of a higher level education institution. The Universidad de Oriente, the second institution of its kind in Cuba, treasures an extensive collection of its heritage bequeathed by its researchers, teachers, and students; however, inefficiencies are detected that limit the use of heritage assets and documents in undergraduate and postgraduate courses, despite the institution's extensive trajectory, the extensive existing collection and the historical, architectural and heritage values it possesses are unknown. To this end, in this paper we propose and present a cultural heritage management platform that, in addition to disseminating the institution's heritage values, promotes the use of the results of research on the nation's cultural heritage in the training processes in different degree courses, research, and relations with institutions in the cultural sector. Theoretical methods, empirical techniques, the Extreme Programming software development methodology and the use of expert criteria and heuristic evaluation for its validation has been applied to assess its impact.

Today, there are billions of connected IoT devices and their number continues to grow as they contribute to the digitalization of infrastructures. However, the deployment process of these smart wireless devices when delivered to customer premises is slow and error prone as each of them needs to be provisioned with authentication credentials to access the corporate network. In this paper, we propose HANNA, a human-friendly provisioning and configuration framework for smart devices, that extends the zero-touch paradigm to large IoT deployments by introducing voice assisted configuration in combination with large scale ad-hoc communications to overcome the initial installation effort of IoT deployments. The most prominent role in HANNA is played by the assisting device, which includes a voice assistant capable of correctly understanding a minimum number of keywords required for initial provisioning and configuration of the devices. The device’s role is to interact with the user and ensure that all provisioning details are received. These are then converted into appropriate machine instructions for further use by the mass provisioning mechanism. We provide an example prototype implementation of HANNA and evaluate the performance of the assisting device in the human-to-machine communication phase and the performance of the selected communication technique in the machine-to-machine communication phase. Our results show the potential of existing speech-to-text engines for this application area and also reveal shortcomings with respect to the robustness of the engines in office-like working environments as well as with respect to user’s gender and language proficiency level. Additionally we show that the proposed machine-to-machine provisioning approach is always faster compared to manual provisioning for cases with more than ten devices.

The 5G ecosystem is comprised of the cellular 5G System, as well as a managed and orchestrated infrastructure providing virtualized network and service functions. The automotive industry with its stringent requirements for connected vehicles is a promising and yet challenging consumer of such 5G ecosystem. Deployment of service instances at distributed cloud resources of cellular network infrastructure edges enables localized low-latency access to these services from moving vehicles but comes along with challenges, such as the need for fast reconfiguration of the distributed deployment according to mobility pattern and associated service and resource demand. In this paper, we investigate a solution for the collaborative orchestration of services for Connected, Cooperative and Automated Mobility (CCAM) within such 5G ecosystem. A key objective is the service continuity for a highly dynamic automotive scenario, achieved by the associated management and orchestration of these services in distributed edge clouds. The proposed solution leverages a multi-tier orchestration system as well as localized management and protocol operations for collaborative edge resources. By means of both analytical and experimental evaluations, the paper draws conclusions on the gain in accelerating orchestration decisions and enforcements, while balancing associated protocol and computational load over the highly distributed and multi-layered orchestration system.

Deterministic performance and reliable operation are vital for many applications with industrial-grade requirements. Such applications rely on Time-Sensitive Networking (TSN) to enable time-critical deterministic communication. While standardization efforts were focused mainly on TSN features for wired domains, recent advances in wireless technologies (e.g., Wi-Fi 6/7) are extending time-sensitive communication towards wireless networks as well. However, achieving multi-domain LAN/ Wireless LAN (WLAN) end-to-end TSN communication requires addressing challenges on end-to-end time synchronization, multi-domain control plane interoperability, run-time end-to-end scheduling, and fine-grained monitoring. Because state-of-the-art TSN controllers’ scope lays far below these new required capabilities, in this work we present a novel, fully-programmable controller for end-to-end TSN-enabled networks. Our controller is based on a modular architecture to be adaptable to challenges arising when shifting the standard TSN scope towards WLAN domain. We deploy a proof-of-concept in a cloud-wired environment to evaluate its key performance indicators when handling increasing numbers of nodes and simultaneous requests. Further, we run experiments on real TSN-enabled networks comprising Ethernet and Wi-Fi technologies, demonstrating the effectiveness of the controller in performing seamless fine-grained traffic control in both domains.

Next-generation mobile networks are expected to flaunt highly (if not fully) automated management. To achieve such a vision, Artificial Intelligence (AI) and Machine Learning (ML) techniques will be key enablers to craft the required intelligence for networking, i.e., Network Intelligence (NI), empowering myriad of orchestrators and controllers across network domains. In this paper, we elaborate on the DAEMON architectural model, which proposes introducing a NI Orchestration layer for the effective end-to-end coordination of NI instances deployed across the whole mobile network infrastructure. Specifically, we first outline requirements and specifications for NI design that stem from data management, control timescales, and network technology characteristics. Then, we build on such analysis to derive initial principles for the design of the NI Orchestration layer, focusing on (i) proposals for the interaction loop between NI instances and the NI Orchestrator, and (ii) a unified representation of NI algorithms based on an extended MAPE-K model. Our work contributes to the definition of the interfaces and operation of a NI Orchestration layer that foster a native integration of NI in mobile network architectures.

Cooperative Intelligent Transportation Systems (CITS) deploy the Cooperative Awareness Basic Service to exchange Cooperative Awareness Messages (CAMs) among road C-ITS entities, e.g., vehicles and roadside units (RSUs). CAMs support vehicular safety and traffic efficiency applications that require continuous status information of surrounding C-ITS entities. These messages provide awareness of traffic information in the Non-Line of Sight (NLOS) of the vehicle and are an essential feature to guarantee safety in vehicles. Typically, applications to display CAMs depend on specific C-ITS hardware and software, which implies that each researcher and the industry must develop an application in software and hardware. In this paper, we propose a framework to support research on CAMs through a monitoring dashboard, deploying a portable environment named CAM Application Framework (CAMAF); it manages the received CAMs and updates a corresponding specific monitor for each active C-ITS entity. Each monitor is configurable by choosing CAM fields and making or changing algorithms to display the desired information. We have tested our proposal in a C-ITS testbed with real live traffic.

In this paper we demonstrate a framework to support research on Cooperative Awareness Messages (CAMs) through a monitoring dashboard, deploying a portable environment named CAM Application Framework (CAMAF); it manages the received CAMs and updates a corresponding specific monitor for each active Cooperative Intelligent Transportation System (C-ITS) entity. Each monitor is configurable by choosing CAM fields and making or changing algorithms to display the desired information. We have tested our proposal in a C-ITS testbed with real live traffic in the SmartHighway localted in Antwerp, Belgium.

In this article, we analyze the use of non-intrusive monitoring sensors to detect dangerous situations in social events, a typical example being sexual harassment situations during parties. Non invasive sensors can be defined as devices that help measuring some specific parameter of interest remaining hidden or un-perceived by the user.We propose a solution based on a social application that can, in a non-intrusive way, detect sexual harassment situations and generate alerts automatically to the close by people so that detection of these situations can become quick and independent from the person being attacked. Our results are still preliminary but show that the project can certainly be developed into a working system that can help prevent control crimes from happening at public gatherings.

One of the aims of the H2020 5G Blueprint project is to enable seamless cross border teleoperation use cases with 5G technology. The explored use cases are automated barge control, automated drive-in-loop docking, cooperative adaptive cruise control based platooning, and remote take-over operations. In this paper, we present an analysis of the network requirements of such use cases, and we present an end-to-end 5G architecture (with a focus on network slicing and seamless cross-border roaming) for the trial network based on lasted standardization work and implementation tools, from user equipment, radio, transport, core network to exposed network APIs. This work will be used as guideline for further network deployment and feature implementation in test labs and project pilot area within the project.

Unmanned Aerial Vehicles (UAVs) are being developed and researched to be used in different areas and diverse use cases. The delivery of medical parcels is an emergency service that avails from the air traffic for the fast delivery of critical health content. Besides 5G networks being the target communication technology for the UAV parcel transport, the infrastructure is still not optimized for ground-level User Equipments (UEs). Using multiple Radio Access Technology (RAT) by UAVs is an alternative solution for scenarios where the 5G network is unstable or not yet fully deployed. In this paper, we present bench-marking of 5G and 4G networks on unideal radio scenarios to understand the real latency and throughput delivered by these technologies in worst-case scenarios. The results show that the 5G network can provide the necessary Quality of Service (QoS) for UAV operations even with Reference Signal Received Power (RSRP) in the order of -90. In comparison, the preliminary data for the 4G network validates that the 4G network could not provide the required latency.

The plethora of heterogeneous and diversified services in 5G and beyond requires from networks to be flexible, adaptable, and programmable, i.e., to be able to correspondingly adapt to changes. As human intervention might significantly increase delays in MANagement and Orchestration (MANO) operations, automation and intelligence become imperative for orchestrating services and resources, especially the ones with stringent requirements for latency and capacity, such as Vehicle-to-Everything (V2X) services. As virtualization and Artificial Intelligence (AI) promise to mitigate those challenges towards enabling true automation in MANO operations, in this paper we present our effort towards building and fully utilizing the real-life testbeds, such as Smart Highway and Virtual Wall, located in Belgium, to conduct realistic experimentation and validation of distributed orchestration intelligence in a dynamic network such as V2X system.

As manual Management and Orchestration (MANO) of services and resources might delay the execution of MANO operations and negatively impact the performance of 5G and beyond Vehicle-to-Everything (V2X) services, applying AI in MANO to enable automation and intelligence is an imperative. The Network Function Virtualization (NFV), Software Defined Networking (SDN), and Artificial Intelligence (AI), could all together mitigate those challenges, and enable true automation in MANO operations. Thus, in this demo paper we will showcase the use of real-life testbed environments (Smart Highway and Virtual Wall, Belgium) and the Proof-of-Concept that we build to conduct realistic experimentation and validation of intelligent and distributed MANO in a dynamic network such as a V2X system.

Design Thinking arises from applying techniques and phases of the design process adapted to marketing, commercial, business and industrial processes, and in recent years it has been contextualised in training processes. This research shows how this approach can constitute a way of working in the context of training computer science lecturers. Our proposal is based on identifying techniques for the transit through the different stages from the didactics of the teaching media to the educational research. We developed a concrete set of actions to develop and research academic/productive skills and competencies in the context of solutions to problems of pedagogical practice. It goes through the identification of academic training objectives, problem analysis, strategy design, the conceptualisation of digital didactic media and constant verification of satisfaction levels, all under a multidisciplinary, collaborative and creative approach in tune with the current times.

The service placement problem considers the placement of multiple connected services across a heterogeneous device network and is one of the core problems of fog computing. We discuss the complexity of this service placement problem, and propose a model for solving it using Multi-Objective Reinforcement Learning (MORL) methodologies. Using a trained neural network greatly reduces the resource consumption of the placement algorithm, making it viable for resource-constrained scenarios. Starting from state-of-the-art techniques, we develop a generic max reward formulation model and apply several MORL methodologies, which solve the placement problem in scenarios where the preference weights change. We compare the results to a baseline methodology and showcase the value of MORL on the placement problem.

Precision time protocol (PTP), a state-of-the-art clock synchronization protocol primarily designed for wired networks, has recently gained attention in the wireless community, due to the increased use of the IEEE 802.11 wireless local area networks (WLAN) in real-time distributed systems. However, all the existing WLAN-based PTP designs either incorporate software timestamping (TS) delivering poor clock synchronization accuracy, or hardware (HW) TS providing better synchronization accuracy at the cost of a significant amount of HW overhead. Moreover, the performance of the existing PTP solutions is mostly evaluated in single-hop wireless networks, while the performance across wired and wireless networks is taken for granted. In this article, a new software-defined-radio-based approach to implement PTP is introduced and validated for the IEEE 802.11 WLAN. Instead of using a dedicated HW clock, the solution utilizes the timing synchronization function clock, an existing clock in the IEEE802.11 standard for synchronization between access point and WLAN stations. The performance of the proposed solution is first investigated within a single-hop WLAN and then across wired–wireless networks. Experimental results unveil that 90% of the absolute clock synchronization error falls within 1.4$\mu \mathrm{s}$.

In the coming years, connectivity between vehicles with autonomous driving features and roadside infrastructure will become more and more a reality on our roads, pursuing to improve road safety and traffic efficiency. In this regard, two main communication standards are considered as key enablers, that is ITS-G5 (based on IEEE 802.11p) and C-V2X (3GPP). To assess the real performance of these technologies, there is still need for an objective and independent one-to-one comparison of these technologies using off-the-shelf hardware under identical and real-life traffic conditions. Until today, performance evaluations are limited to simulations, emulations or individual technology assessments in real-life circumstances. In this paper, an exhaustive and fair evaluation of the technologies has been conducted in a real-life highway environment under identical conditions. Tailored evaluation tools in combination with our in-house CAMINO vehicular framework has been utilized to perform the tests and analyze the results for different well-specified test cases. The performance evaluation shows that for the short-range technologies, C-V2X PC5 has, in general, a higher range than ITS-G5, while ITS-G5 offers lower latency than C-V2X PC5 in low-density scenarios. Long-range 4G C-V2X can be considered as an alternative for certain use cases. The outcome of this experimentation study can be used as valuable information for the further development of future (5G) connected and autonomous driving.

With the increasing traffic of Video on Demand (VoD), network providers are seeking to deliver high Quality of Experience (QoE) for their users. Many methods have been proposed to assess VoD-related QoE. Some of them rely on client instrumentation and reporting QoE information to network elements, such as Server and Network Assisted DASH, others are based on statistical methods that make QoE inferences using monitored network conditions, such as throughput and delays. In this article, we present a practical method to estimate QoE for VoD using the widely supported Internet Control Message Protocol (ICMP) probes. Measured network conditions are used as input to a Machine Learning (ML) model that estimates QoE in terms of Mean Opinion Score (MOS), based on the ITU-T P.1203 Recommendation. The estimation encompasses video quality switches and playback stalls. We estimate MOS with an average Root Mean Square Error (RMSE) of 1.05 for a catalog of 25 different videos, training a model with sessions of the shortest video, and evaluating the generalization to the full catalog. We performed experiments using a virtualized setup as well as in a Wide Area Network.

Network slicing is the 5G research field that addresses the services requirements compliance over the same network. In order to robustly and securely manage the different slices in a network, we propose to use blockchain, a distributed structure that stores data without the need of an external entity to ensure data integrity and reliability. In this paper, we present a proposal on deploying a network slicing solution for Non-Public Networks (NPNs) in health environments using blockchain technology. Our solution aims to provide both performance isolation over wireless networks and privacy.

5G systems promise to enable autonomous vehicles by empowering road-, water-, and air-vehicles with ultra low latency communications and computing at edge in order to share and process data from multiple sensors. However, in order to realize such fully Connected and Automated Mobility (CAM) for cars, drones and vessels, a crucial intermediary step must be fully achieved: 5G-based tele-operated transport. In order to do so, the European project H2020 5G-Blueprint aims to design, test, and validate in real deployments a 5G-enabled tele-operated transport and its enabling functions in both a relevant and operational environment realised through cross-border trials on the road and on the water along 5G corridors in the Dutch and Belgian border area, resulting in a blueprint for future cooperation on 5G-enabled CAM between public, private and semi-private parties (e.g, ports), gaining new and innovative insights on the stringent particular requirements for safe CAM, on the architecture, on governance and relevant business models.

New challenges in communications are being raised as autonomous vehicles are gaining popularity. Higher data rates, connection reliability and real-time responses are challenges to be undertaken on the next years and, although they have been researched over the past decade, no solution that addresses all of them simultaneously has been widely accepted. In this paper, we reintroduce ORCHESTRA, an all-in-one solution to manage devices with multi-connectivity capabilities, while also enabling the use of techniques such as load balancing, seamless handovers and package duplication to address the challenges aforementioned. Furthermore, we also present a list of additions and improvements that would enable this solution to respond to the newest communication challenges.

With the advancements in SDN and NFV, both applications and network functions can be re-designed, and deployed at more appropriate locations. Thanks to the MEC platforms, cloud-alike service deployments are offered to the users/vehicles at closer proximity. However, MEC deployments are usually i) constrained in resources, ii) contain heterogeneous and distributed network and computing resources, and iii) cover narrower region that constrains service continuity due to the high mobility of vehicles. Thus, in this paper, we present our approach on collocating MEC platforms with roadside infrastructure (i.e., RSUs) in order to improve the QoS of infotainment services for vehicles on the smart highway. We tackle both challenges presented above by deploying MEC platforms along the highway, thereby having distributed control over each MEC host in the form of Kubernetes master nodes, and one powerful and yet centralized orchestrator in the cloud. Our approach is one of the earliest attempts to collocate MEC with the RSU, and to test the benefits of the smart application placement in a realistic vehicular environment.

The benefits of Cooperative Intelligent Transport Systems (C-ITS) span a range of areas, such as road safety and service reliability. Thus, traffic accidents can be avoided, and ultimately human lives can be saved. As C- ITS disseminate in-vehicle information, an error in the disseminated velocity accuracy may cause accidents, particularly in the automated and autonomous driving contexts. Hence, it is vital to detect these errors and warn vehicles about the inaccuracy of the speed reported by the vehicle in the C- ITS Cooperative Awareness Message (CAM). To illustrate the potential of our solution, we present one use case with platooning green light priority and another to save the life of a cyclist when it jumps into the lane of a vehicle without signaling or without enough space to do so. Our solution integrates a Roadside Unit (RSU) with a velocity detection device to enforce the accuracy of the disseminated velocity from a vehicle using C- ITS. We compare the velocity disseminated by a vehicle, via CAMs, with the velocity acquired from a standard deployed “speed detection device”. To show the feasibility of our proposal, we emulate a vehicle sending and receiving C- ITS messages in a virtualized environment. The RSU receives C- ITS messages to get the disseminated velocity of a vehicle and, if needed, warn that the disseminated velocity is inaccurate. Focusing on our experiments, it takes less than 500 μ$s$ to process the information and report the inaccuracy. Additionally, our work introduces a data calibration warning that could be needed by autonomous vehicles.

Vehicle to Everything (V2X) systems are starting to be used in some regions worldwide; consequently, this leads to the consolidation of its major components, putting into practice all the potential of V2X. The Smart Highway testbed located in Antwerp, Belgium, makes available V2X systems for experimentation and evaluation, providing flexible support to different V2X access technologies simultaneously. Namely, ITS-G5 for short-range communication and Cellular Vehicle to Everything (C-V2X) for short- and long-range communication. Smart Highway testbed applications are needed to explore, support, and disseminate research opportunities. It motivates us to make available examples of testbed applications as monitoring tools for researchers and industry. Our solution makes available a toolkit using a graphical interface to monitor C-ITS Cooperative Awareness Messages (CAMs) and Decentralized Environmental Notification Messages (DENMs) in a user-friendly way. An additional application plots the current location of C-ITS entities in our Local Dynamic Map (LDM).

The educational courses that fall into Science, Technology, Engineering and Math (STEM) category require an extensive practical training in laboratories, in order to build and strengthen students’ skills, thereby preparing them for a future job market. In particular, the significant advancements in computer science and engineering press an urgent need to rethink the core of the existing academic courses, their objectives, and the tools for the practical work, due to the need to maintain the balance between the knowledge that academia provides to the students and the actual requirements for students’ future job vacancies. To this end, our educational research includes the design and development of two different types of laboratories, i.e., a low-cost Raspberry Pi-based laboratory, and a laboratory in the cloud, for the practical teaching of the course Distributed systems. In this paper, we present the valuable feedback from our undergraduate students for both types of the aforementioned experimentation approaches, thereby unraveling the pros and cons of both, and analyzing the existing challenges that still need to be properly tackled.

Recent developments in nanotechnology herald nanometer-sized devices expected to bring light to a number of groundbreaking applications. Communication with and among nanodevices will be needed for unlocking the full potential of such applications. As the traditional communication approaches cannot be directly applied in nanocommunication, several alternative paradigms have emerged. Among them, electromagnetic nanocommunication in the terahertz (THz) frequency band is particularly promising, mainly due to the breakthrough of novel materials such as graphene. For this reason, numerous research efforts are nowadays targeting THz band nanocommunication and consequently nanonetworking. As it is expected that these trends will continue in the future, we see it beneficial to summarize the current status in these research domains. In this survey, we therefore aim to provide an overview of the current THz nanocommunication and nanonetworking research. Specifically, we discuss the applications envisioned to be supported by nanonetworks operating in the THz band, together with the requirements such applications pose on the underlying nanonetworks. Subsequently, we provide an overview of the current contributions on the different layers of the protocol stack, as well as the available channel models and experimentation tools. Finally, we identify a number of open research challenges and outline several future research directions.

Current industrial Internet of Things (IoT) demands are calling for more flexible and programmable networks that ensure high reliability in dynamic mission-critical scenarios. Centralized software-defined networking (SDN) offers high levels of flexibility and programmability that traditional distributed IoT protocols cannot offer. However, the use of SDN in IoT is currently not really lifting off due to wireless links unreliability, excessive control overhead, and devices' limited resources. In order to reduce the impact of these issues, Whisper enables SDN-like capabilities in IoT by centrally controlling the distributed routing and scheduling planes in the IoT network (things overlay). To do so, the Whisper controller carefully sends computed messages compatible with the standardized distributed protocols already running in the network that change the default protocols' behavior. However, as many other SDN-on-IoT approaches, Whisper is currently limited to the IoT network scope and remains as yet another independent network management silo. In this article, we argue that IoT network control should be jointly coordinated by the same SDN instance that also manages the wired segments. In order to do so, we present a new fully programmable solution that shifts the Whisper scope from the edge to the core, deploying and testing such architecture in real-world large-scale testbeds. We use 6TiSCH as industrial IoT enabler and the open network operating system platform to orchestrate all network segments. Finally, we report the technical challenges, discussing the lessons learned, and demonstrating the feasibility and suitability of this Whisper-based solution to provide an efficient and programmable end-to-end control over a heterogeneous network domain.

Most industrial applications demand determinism in terms of latency, reliability, and throughput. This goes hand in hand with the increased complexity of real-time network programability possibilities. To ensure network performance low-overhead, high-granularity, and timely network verification techniques need to be deployed. The first cornerstone of network verification ability is to enable end-to-end network monitoring, including end devices too. To achieve this, this article shows a novel and low overhead in-band network telemetry and monitoring technique for wireless networks focusing on IEEE 802.11 networks. A design of in-band network telemetry enabled node architecture is proposed and its proof of concept implementation is realized. The PoC realization is used to monitor a real-life SDN-based wireless network, enabling on-the-fly (re)configuration capabilities based on monitoring data. In addition, the proposed monitoring technique is validated in terms of monitoring accuracy, monitoring overhead, and network (re)configuration accuracy. It is shown that the proposed in-band monitoring technique has 6 times lower overhead than other active monitoring techniques on a single-hop link. Besides this, it is demonstrated that (re)configuration decisions taken based on monitored data fulfill targeted application requirements, validating the suitability of the proposed monitoring technique.

Current Software Defined Networking (SDN) techniques allow improving network control and flexibility. However its use in IoT is not trivial because IoT networks are unreliable and highly resource-constrained. Among some of the existing solutions proposed in the literature, Whisper enables SDN-like control over the packet forwarding and cell allocation of IoT devices by injecting in the network artificial, but still standard compliant messages that alter the default protocol behavior. Since Whisper uses carefully computed routing and scheduling messages that are compatible with the distributed protocols run in the network, it reduces the overhead in the network and operates without modifying the IoT devices' firmware. However, as other SDN-on-IoT technologies, Whisper is currently limited to the IoT network scope and remains as yet another independent network management silo. In this paper we propose a new higher-level architecture that allows to fully integrate the IoT SDN network management into a network operating system, such as ONOS, by using Whisper in order to provide an integral end-to-end softwarization. We also describe the interaction between the Whisper platform and the orchestrator and test our solution with real 6TiSCH-compatible hardware in the ONOS platform. Finally, we discuss the requirements and technical challenges to fully leverage Whisper to provide an efficient and programmable end-to-end control over an heterogeneous network domain.

Due to the varying conditions in traffic and resource availability in networks nowadays, maintaining continuity of network service and satisfying QoS and QoE requirements became a challenging task. If considered in a highly diversified environment in terms of technology and administration, it gets even more complicated, and appropriate service and resource management solutions are mandatory. Thus, the aim of this paper is to present our specific perspective of the ongoing European H2020 5G-CARMEN project, addressing the importance of proactive reconfiguration of network services and their migration between different domains. Leveraging 5G technology and MEC platform, our management platform for automated low-latency-aware VNF placement and migration will enable orchestration of network services and resources across different administrative and technology domains.

An increase of Video on Demand (VoD) consumption has occurred in recent years. Delivering high Quality of Experience (QoE) for users consuming VoD is crucial. Many methods were proposed to estimate QoE based on network metrics, or to obtain direct feedback from video players. Recent proposals usually require monitoring tools installed in multiple network nodes, instrumentation of client devices, updates on existing network elements, among others. We propose a method based on Internet Control Message Protocol (ICMP) probing to estimate QoE for users consuming VoD. The method allows network operators to estimate which QoE level can be delivered to the user according to current network conditions using a Machine Learning (ML) model. Our method does not require installation of software at different network nodes, relying on ICMP probing which is widely supported by existing devices. Our QoE inference model estimates Mean Opinion Score (MOS) with Root Mean Square Error (RMSE) of 0.98, with additional 27 Kbps of traffic during probing. We evaluate the model's generalization capacity when estimating QoE for videos different from the one used for training, which can speed up model's creation process. In those cases MOS was estimated with RMSE of 1.01.

In today's cities, it is increasingly normal to see different systems based on Artificial Intelligence (AI) that help citizens and government institutions in their daily lives. This is possible thanks to the Internet of Things (IoT). In this paper we present a solution using low-resolution thermal sensors in combination of deep learning to detect people in the images generated by those sensors. To verify whether the deep learning techniques are appropriate for this type of images of such low resolution, we have implement a Faster Region-Convolutional Neural Network. The results obtained are hopeful and undoubtedly encourage to continue improving this research line. With a perception of 72.85% and given the complexity of the problem presented we consider the results obtained to be highly satisfactory and it encourages us to continue improving the work presented in this article.

The computer network industry is preparing for the revolution caused by the virtualization of the network infrastructure. The virtualization of network functions, together with the network programmability, is a fundamental skill-set required in both electrical engineers and computer scientists, which will become even more significant in the upcoming years. Therefore, in this article, we present an educational framework for Service Function Chaining (SFC) practical teaching to undergraduate students aiming to prepare them for future Information and Communication Technologies (ICT) and communication networks market that will demand skillful professionals in the domain. The educational framework was designed for the Network Management course at the University of Antwerp. To structure the content of the framework’s sessions, we explain the fundamental concepts behind the SFC. Moreover, we detail the framework objectives, sessions, and how we plan to evaluate it. We also present facts and works that endorse the hands-on teaching preparing students for the research and development in the new generation communication networks arena. After, we discuss the methodology of the educational framework alignment with joint ACM/IEEE Computer Engineering Curricula for Network Management courses. Next, we present possible outcomes during the course evaluation using the survey. We conclude which this educational framework achieves the necessary core learning outcomes to empower students concerning the practical steps when deploying and managing communication networks and the virtualization techniques which nowadays are being applied.

Dynamic Spectrum Access allows using the spectrum opportunistically by identifying wireless technologies sharing the same medium. However, detecting a given technology is, most of the time, not enough to increase spectrum efficiency and mitigate coexistence problems due to radio interference. As a solution, recognizing traffic patterns may lead to select the best time to access the shared spectrum optimally. To this extent, we present a traffic recognition approach that, to the best of our knowledge, is the first non-intrusive method to detect traffic patterns directly from the radio spectrum, contrary to traditional packet-based analysis methods. In particular, we designed a Deep Learning (DL) architecture that differentiates between Transmission Control Protocol (TCP) and User Datagram Protocol (UDP) traffic, burst traffic with different duty cycles, and traffic with varying rates of transmission. As input to these models, we explore the use of images representing the spectrum in time and time-frequency. Furthermore, we present a novel data randomization approach to generate realistic synthetic data that combines two state-of-the-art simulators. Finally, we show that after training and testing our models in the generated dataset, we achieve an accuracy of ≥ 96 % and outperform state-of-the-art methods based on IP-packets with DL.

The combination of 5G and Multi-access Edge Computing (MEC) technologies can bring significant benefits to vehicular networks, providing means for achieving enhanced Quality of Service (QoS), and Quality of Experience (QoE) of wide variety of vehicular applications. Although beneficial in terms of latency reduction, the edge of the architecture for communication networks produces enormous heterogeneity of network services and resources. This challenge becomes even more severe when different administration domains are taken into consideration. Thus, efficient network Management and Orchestration (MANO) of network resources and services are inevitable. As ETSI provided guidelines and standardization for NFV MANO components, the MEC platform can be used to host network services, while MANO systems are in charge of network service management and orchestration. In this paper, we focus on the specific impact that the Virtualized Infrastructure Manager (VIM) has on the performance of the whole MANO system, used for management and orchestration of MEC services and resources in vehicular networks by enabling the on-demand service instantiation, and service teardown. In our testbed-based evaluation, we measured the network service instantiation and termination delays when evaluating: a) OpenStack and Amazon Web Services (AWS) as VIMs for Open Source MANO (OSM), and b) OpenStack and Docker in case of Open Baton. Such performance analysis with a strong experimental component can serve as a baseline for researchers and industry towards exploiting the opportunities that existing MANO solutions provide.

The fifth generation of mobile networks (5G) and the Software- Defined Radio Access Networks (SD- RAN) architecture envision to support lower latency, enhanced reliability, massive connectivity, and improved energy efficiency. In this context, low latency is considered crucial and Ultra-Reliable Low Latency Communication (URLLC) as one of the key enablers. Currently, IEEE 802.11 networks cannot be programmed fine-grained enough nor manage multiple networks at runtime. Besides, in such scenarios, the coarse-grained level of monitoring information has been hindering troubleshooting and management. In this paper, we present an SDN-based framework where fine-grained End-to-End (E2E) network statistics can be gathered using Inband Network Telemetry (INT) and used for network control and management. With such fine-grained network information, we show how our system can enhance the Quality of Service (QoS) delivery through slice orchestration in IEEE 802.11 Radio Access Networks (RANs).

Over the last few years, different researchers have been developing protocols and applications in order to land unmanned aerial vehicles (UAVs) autonomously. However, most of the proposed protocols rely on expensive equipment or do not satisfy the high precision needs of some UAV applications, such as package retrieval and delivery. Therefore, in this paper, we present a solution for high precision landing based on the use of ArUco markers. In our solution, a UAV equipped with a camera is able to detect ArUco markers from an altitude of 20 meters. Once the marker is detected, the UAV changes its flight behavior in order to land on the exact position where the marker is located. We evaluated our proposal using our own UAV simulation platform (ArduSim), and validated it using real UAVs. The results show an average offset of only 11 centimeters, which vastly improves the landing accuracy compared to the traditional GPS-based landing, that typically deviates from the intended target by 1 to 3 meters.

A variety of nanoscale applications require downlink and broadcast-based transmission of control packets from a powered transmitter to energy-harvesting nanonodes with constrained storage capacity. The nanonode's communication system is anticipated to be a bottleneck for such nanonetworks, hence an accurate modeling of its energy consumption is needed. Currently, TS-OOK is a prevailing scheme for nanocommunication in the terahertz (THz) frequencies, with short pulses representing logical "1"s and silences logical "0"s. In the energy modeling of this scheme, certain energy consumptions are attributed to the transmission and reception of the pulses. However, traditional communication systems teach us that the idling energy consumption should not be neglected. Hence, we provide an energy consumption model for TS-OOK-based nanocommunication systems that accounts for the energy consumed in idling, in addition to the transmission and reception-originated consumptions. We demonstrate that, for a meaningful performance of the considered nanonetwork, the nanonode's idling energy consumption has to be at least nine orders of magnitude lower than the corresponding energy consumption in reception. To increase the tolerable idling energy, we propose a new energy lifecycle for the receiving nanonodes. Assuming frequent packet repetitions on the transmit side, the proposed lifecycle utilizes periodic short wake-ups of the receiving nanonodes. We show that, when the proposed lifecycle is utilized, up to three orders of magnitude higher idling energy consumption can be tolerated compared to the baseline.

Electric Vehicles (EVs) sales are increasing in the recent years due to several factors such as cost reduction, fuel cost increase, pollution reductions, government incentives, among others. At the same time, Intelligent Transportation Systems (ITS) are continuously improving, and researchers use different simulators in order to test their proposals before implementing them in real devices. However, traditional communications-aimed simulations do not include fuel consumption issues that are a key factor in transportation systems. This paper presents the addition of Electric Vehicles consumption to the ns-3 simulator, which currently is one of the most used network simulators. Our proposal follows all the models, coding style, as well as engineering guidelines of ns-3, coupled with the characteristics of each vehicle, to accurately estimate the energy consumption. We also analyze the performance of our proposal while simulating a part of the E313 highway, located in Antwerp, Belgium. In particular, we compare the ns-3 results obtained in terms of energy consumption to those obtained in SUMO. In addition, we study the impact of our proposal on the overall simulation time.

Quality of Experience (QoE) should be the driver for network orchestration in 4G networks. At the same time, the network must be able to cope with high bandwidth requirements from applications such as video streaming, while dealing with a large number of users. This paper proposes a network orchestrator that adjusts network parameters to improve QoE of video streaming. The orchestrator uses Device-to-Device (D2D) communication to improve user's QoE, also reducing the demand on 4G network. The use of D2D is triggered by a machine learning engine. Experiments made in a physical testbed show an improvement on the mean horizontal video resolution from 768 to 1280 pixels, as well as a decrease of around 90% at the impact on the QoE, considering the number of video resolution changes. Finally, the demand on the network backhaul is decreased by around 38%.

An important aspect of managing multi access point (AP) IEEE 802.11 networks is the support for mobility management by controlling the handover process. Most handover algorithms, residing on the client station (STA), are reactive and take a long time to converge, and thus severely impact Quality of Service (QoS) and Quality of Experience (QoE). Centralized approaches to mobility and handover management are mostly proprietary, reactive and require changes to the client STA. In this paper, we first created an Software-Defined Networking (SDN) modular handover management framework called HuMOR, which can create, validate and evaluate handover algorithms that preserve QoS. Relying on the capabilities of HuMOR, we introduce ABRAHAM, a machine learning backed, proactive, handover algorithm that uses multiple metrics to predict the future state of the network and optimize the load to ensure the preservation of QoS. We compare ABRAHAM to a number of alternative handover algorithms in a comprehensive QoS study, and demonstrate that it outperforms them with an average throughput improvement of up to 139%, while statistical analysis shows that there is significant statistical difference between ABRAHAM and the rest of the algorithms.

In this paper, we present the Dynamic Small cell Management (DSM) scheme to improve vehicular communications, focusing on the dynamic allocation of small cells when the macrocells cannot cope with the traffic generated by the connected cars. We have considered real base station deployments in the city of Dublin, Ireland, combined with realistic models of vehicle mobility, and small cell deployments. Simulation results demonstrate that our DSM scheme improves communication capabilities (the number of messages correctly received by the infrastructure increases up to a 43.75%), while it also reduces BS overloading (the number of messages managed by the base stations is reduced up to an 8.72%). Therefore, the use of this smart and dynamic solution not only benefits vehicles' communications but also mobile operators.

In Vehicular Networks, some applications require a fast and reliable warning data transmission to the Emergency Services and Traffic Authorities. Nevertheless, communication is not always possible in vehicular environments due to the lack of connectivity. To overcome these issues (i.e., signal propagation problem and delayed warning notification time), an effective, smart, cost-effective, and all-purpose RSU deployment policy should be put into place. In this paper, we propose GARSUD, a system which uses a genetic algorithm that is capable to automatically provide a Roadside Unit deployment suitable for any given road map layout. Simulation results show that our proposal is able to reduce the warning notification time --the time required to inform emergency authorities in traffic danger situations-- and to improve vehicular communication capabilities in different flows of traffic at different times during the day.

Remote labs and online experimentation offer a rich opportunity to learners by allowing them to control real equipment at distance in order to conduct scientific investigations. Remote labs and online experimentation build on top of numerous emerging technologies for supporting remote experiments and promoting the immersion of the learner in online environments recreating the real experience. This paper presents a methodology for the design, delivery and evaluation of learning resources for remote experimentation. This methodology has been developed in the context of the European project FORGE, which promotes online learning using Future Internet Research and Experimentation (FIRE) facilities. FORGE is a step towards turning FIRE into a pan-European educational platform for Future Internet. This will benefit learners and educators by giving them both access to world-class facilities in order to carry out experiments on e.g. new internet protocols. In turn, this supports constructivist and self-regulated learning approaches, through the use of interactive learning resources, such as eBooks.

In this paper we present an approach towards empowering online telecommunications engineering education by enabling hands-on remote experimentation over Trinity College Dublin's wireless testbed. Moreover, in order to offer a flexible testbed, capable of fulfilling the different and particular requirements of experimenters, we have created a framework that allows the virtualization of our testbed resources to create experimentation units to be used by remote experimenters/learners. Furthermore, we present the FORGEBox framework that offers an environment and resources to create online material capable to access the virtualized and physical testbed resources for incorporating experimentation into HTML-based online educational material.

This paper presents the Forging Online Education through FIRE (FORGE) initiative, which aims to transform the Future Internet Research and Experimentation (FIRE) testbed facilities, already vital for European research, into a learning resource for higher education. From an educational perspective this project aims at promoting the notion of Self-Regulated Learning (SRL) through the use of a federation of highperformance testbeds and at building unique learning paths based on the integration of a rich linked-data ontology. Through FORGE, traditional online courses will be complemented with interactive laboratory courses. It will also allow educators to efficiently create, use and re-use FIRE-based learning experiences through our tools and techniques. And, most importantly, FORGE will enable equity of access to the latest ICT systems and tools independent of location and at low cost, strengthening the culture of online experimentation tools and remote facilities.

Due to the increasing popularity of mobile devices and the growing development of wireless networks, nowadays automobiles are able to communicate among them and with the infrastructure using different wireless technologies, thus improving not only communications but also safety on the roads. In order to improve communications by maintaining the Quality of Service (QoS) required by applications (e.g. throughput, latency) while the car is moving, switching from one base station to another, Vertical Handover techniques are the most adequate solution. In this work we present a Vertical Handover Decision Algorithm powered by the IEEE 802.21 protocol which takes advantage of the current devices deployed in the vehicle's on-board unit by considering geolocation, car navigation and realistic propagation model of different underlying networks such as Wi-Fi, WiMAX, and UMTS. Results demonstrate that QoS can be guaranteed when location and networking parameters (such as packet delay and bandwidth offered) are jointly considered.

Wireless technologies have been widely deployed in the last decade making users demand for continuous connectivity. Users not only demand being attached to a network, but possibly to the one with the highest performance, or at least to a network able to fulfill their requirements. To choose the best network among different candidates, the IEEE 802.21 standard has been developed. In this paper we aim at demonstrating the viability of performing Vertical Handover (VHO) processes based on the IEEE 802.21 protocol. To do so, we have evaluated a VHO strategy which considers network availability and maximum data rate in order to choose the best network candidate among the Wi-Fi, the WiMAX, and the UMTS. Moreover, throughout a set of experiments, we have also evaluated the maximum performance of the available networks.

Nowadays, due to the ubiquity of wireless technologies, users demand continuous connectivity guaranteeing the Quality of Service (QoS) required for their communications. To fulfill those requirements, seamless Vertical Handover (VHO) is performed in order to maintain the connectivity among different wireless technologies while the user equipment moves across different coverage areas. In this work, we present a set of experiments to evaluate the vertical handover performance when relying on the IEEE 802.21 standard in scenarios where Wi-Fi, WiMAX and UMTS technologies are available. Experimental results show that a technology-aware vertical handover mechanism is able to achieve an adequate performance when traffic congestion is low.

Prefetching is an interesting technique for improving web performance by reducing the user-perceived latency when surfing the web. Nevertheless, due to its speculative nature, prefetching can increase the network traffic and the server load. This could negatively affect the overall system performance and decrease the quality of service. To minimize and maintain under control these adverse effects, in this paper we propose an intelligent prefetching mechanism that dynamically adjusts the aggressiveness of the prefetching algorithm at the server side. To this end, we also propose a traffic estimation model that permits to accurately calculate, in the server side, the extra load and traffic generated by the prefetching. The performance evaluation study shows that our proposal effectively regulates the adverse effects of web prefetching without affecting its benefits.