Edge computing is one of the key features of the 5G technology-scape that is realizing new and enhanced automotive use cases for improving road safety and emergency response management. Back Situation Awareness (BSA) is such a use case that provides an advance notification to the vehicles of an arriving emergency vehicle (EmV). This paper presents an algorithm for enhancing the accuracy of the advanced Estimated Time of Arrival (ETA) notification of an approaching EmV towards the other vehicles on the highway. The notification is expected to ensure timely reaction by the vehicles to create a clear corridor for the EmV to pass through unhindered, thereby saving critical time to reach the emergency event in a safe manner. The main features of the presented solution are i) the self-correcting algorithm, ii) adaptive and dynamic dissemination areas size allocation, as a response to traffic changes, and iii) the evaluation of the ETA estimation accuracy. We have used the real travel time data measurements collected on the E313 highway (Antwerp, Belgium), to evaluate the performance of the algorithm. The performance is evaluated and compared in terms of accuracy and run-time complexity, using different methods such as Kalman filter, Filter-less method, Moving Average, and Exponential Moving Average filters. It is observed that the Kalman filter provides better accuracy on the ETA estimation, thereby reducing the estimation error by around 14% on average.

This article presents a concept of a TSN Controller NetApp to support diverse vertical applications and provide Quality of Service guarantees during their life-cycle. The NetApp is composed of a Controller entity that receives and processes requests from vertical applications with specific network performance demands, and an Agent entity which applies configurations and monitors the state of network elements. This control architecture has been extended to support wireless TSN communication on top of openwifi, supporting the flexibility required by vertical applications with mobile devices such as drones and automated guided vehicles. We describe the building blocks of the TSNC NetApp supporting wired-wireless TSN deployments and show its experimental results demonstrating the feasibility of our solution.

A big challenge of autonomous mobility is guaranteeing safety in all possible extreme and unexpected scenarios. For the last 25 years, the sector therefore focused on improving the automation functions. Nevertheless, autonomous mobility is still not part of daily life. The 5G-Blueprint project follows an alternative approach: direct control teleoperation. This concept relies on 5G connectivity to remove the physical coupling between the human driver or sailor and the controlled vehicle or vessel. This way, automation and teleoperation can be combined as complementary technologies, assigning them to different segments of a single trajectory, realizing driverless mobility in a safe, scalable, and cost-efficient manner. However, this mode of operation brings demanding connectivity requirements, such as high uplink bandwidth, low latency and ultra-reliability at the same time, for which the potential of 5G needs to be studied and explored. In this paper, we present our performance validation strategies to pursue 5G-enhanced teleoperation in real-life environment (e.g., public roads, busy sea ports), including some initial results that we collected during the in-country piloting phase.

One of the major challenges in 5G-based Cooperative Connected and Automated Mobility is to ensure continuity of a service that is deployed on the network edge and used by a moving vehicle. We propose enablers for smart cellular edges, which support service continuity in cross-border scenarios by the timely preparation of a service instance in an anticipated topologically closer target edge, and by connecting the vehicle to such service instance before the cellular handover occurs. In this paper, we use the edge data centers of a German and Austrian mobile operator to showcase two main enabling pillars for edge service continuity, i.e., i) transparent edge bridging by means of a programmable data plane to serve a vehicle from the target edge before the vehicle performs handover to a different operator, and ii) smart applications, which apply data analytics to boost orchestration decisions for target edge preparation.

5G Stand Alone (SA) networks are in the process of implementation, as the today's progress of the main business services to migrate to the 5G new services communication (enhanced Mobile Broadband - eMBB, Ultra Reliable Low Latency Communications - URLLC, massive Machine Type Communications - mMTC) is estimated to slowly increase. There have been identified some key aspects responsible for the novel 5G communication adoption process, such as the complexity of the services deployment and the clear understanding of the huge potential of the technology that can further support the 5G vertical's stakeholders. This paper is representing the work of the EU funded project VITAL-5G in deploying 5G Stand Alone 3GPP Rel.16 testbeds, with enhanced network and services capabilities and 5G resources available to be offered to industries vertical's customers. The 5G solution of the testbed design is covering several aspects of the future 5G network implementation, such as services management and orchestration, automation of resources allocation, 5G network slicing (Radio Access Network, Core and Transport) and user traffic prioritization according to the service slice needs, eMBB and URLLC. An important aspect is the availability of the entire 5G ecosystem to be offered to the 5G developers and 3 rd parties for advanced and extensive trials such as Innovative Network Application (N etApps) implementations. By abstracting the complexity of underlying 5G infrastructure, reducing the time of service creation and deployment and optimizing the 5G resource usage, N etApps is a key enabler of 5G adoption.

Intelligent edge orchestration has become a vital component within next generation communication networks, such as 5G. They offer optimal resource allocation and service distribution, hence allow for full utilization of the opportunities provided by those networks. Orchestrators make use of Machine Learning (ML) techniques to determine the most optimal operational decisions, such as deployment and scaling of services, ensuring the quality of the service performance. The training and validation of these models require significant amount of data. However, in such environments we deal with heterogeneous and distributed data sources, in which the data needs to be collected and pre-processed efficiently, and as such, made ready-to-use for these ML models. Hence, in this paper several state-of-the-art data management technologies suitable for edge computing and orchestration are investigated and compared. After investigating the theoretical features of these technologies, they are deployed and tested on the Smart Highway testbed. The strengths and shortcomings of these systems are presented and compared based on the Quality of Service (QoS) requirements for various vehicular services dealing with highly mobile users, i.e., vehicles, which are considered as quite stringent. This hybrid platform, combining several data management technologies, will be used to assist and enrich the research on smart edge orchestration at IDLab and serve as a reference for other interested parties.

Network slicing plays a key role in supporting the influx of smart devices and ensuring their connectivity, because it divides the network into different logical slices over the same shared infrastructure with the aim to guaranty Quality of Service (QoS), security and isolation, which are some of the challenges that IoT systems face. The aim of this paper is to research the possibilities using Network Slicing (NS) towards enabling healthcare services with secured data flows from smart devices to cloud and then back to end user, assuring security, isolation and the required levels of QoS. In this paper, we present the study of: i) isolation and security of network slices, which is important due to interference that occur between slices and can effect the data privacy, ii) service requirements, such as e.g., latency requirement for healthcare services, which is important to have an efficient diagnosis and rapid decisions in case of any risk, and iii) building a hybrid network slicing system, combining different technologies and applying slicing, which could bring the optimal solution for end-to-end for smart healthcare and smart living.

The next generation of vehicular communications has the potential to interconnect Unmanned Automated Vehicles (UAVs), which are the level 5 autonomous vehicles, with its nearby surroundings including Vulnerable Road Users (VRUs), other UAVs, and roadside infrastructure. Given the increased interest in the demand of autonomous driving and Cooperative Connected and Automated Mobility (CCAM), the future UAVs will be safer through enabling communication between UAVs themselves and with their surroundings e.g., VRUs. Communication between UAVs and VRUs will be needed since VRUs will not be able to make a physical eye-contact with the UAVs since their is no physical driver anymore behind the steering wheel. In this way the VRUs can make themselves digitally aware in the UAV traffic and vice versa. Such that UAVs will not be isolated black boxes, i.e., operating and relying fully on their embedded sensors, to its surrounding UAVs and VRUs. This paper shows the current and future needs for autonomous vehicular communication between UAVs and VRUs. Therefore, we present the current trends in the vehicular communication domain and its research challenges. We propose a solution to enable communication between semi-autonomous vehicles, UAVs and their surrounding e.g., VRUs. In our paper, we provide also an early initial proposal to fuse the Vehicle-to-everything (V2X) communication with the embedded autonomous software in the autonomous vehicle.

Intelligent edge orchestration has become a vital component within next generation communication networks, such as 5G. They offer optimal resource allocation and service distribution, hence allow for full utilization of the opportunities provided by those networks. Orchestrators make use of Machine Learning (ML) techniques to determine the most optimal operational decisions, such as deployment and scaling of services, ensuring the quality of the service performance. The training and validation of these models require significant amount of data. However, in such environments we deal with heterogeneous and distributed data sources, in which the data needs to be collected and pre-processed efficiently, and as such, made ready-to-use for these ML models. Hence, in this paper several state-of-the-art data management technologies suitable for edge computing and orchestration are investigated and compared. After investigating the theoretical features of these technologies, they are deployed and tested on the Smart Highway testbed. The strengths and shortcomings of these systems are presented and compared based on the Quality of Service (QoS) requirements for various vehicular services dealing with highly mobile users, i.e., vehicles, which are considered as quite stringent. This hybrid platform, combining several data management technologies, will be used to assist and enrich the research on smart edge orchestration at IDLab and serve as a reference for other interested parties.

By delivering end-to-end latencies down to 5ms, data rates of up to 20Gbps, and ultra-high reliability of 99.999%, 5G is extending the capabilities of numerous industry verticals, including the Transport & Logistics (T&L). As the T&L industry has a pivotal role in modern production and distribution systems, it is expected to leverage 5G technology to significantly increase efficiency and safety in the T&L operations, through automating and optimizing processes and resource usage. However, to be able to truly benefit from 5G, the design, the development, as well as the management, of T&L services need to specify and include 5G connectivity requirements, and the features that are tailored to the specific T&L use cases. To this end, in this paper we introduce the concept of Network Applications (NetApps), as the fundamental building blocks of T&L services in 5G, which simplify the composition of complex services, abstracting the underlying complexity and bridging the knowledge gap between the vertical stakeholders, the network experts, and the application/service providers, while specifying service-level information (vertical specific) and 5G requirements (5G slices and 5G Core services). In this paper, we exemplify the concept of NetApps leveraging one of the VITAL-5G use cases, which provides faster and safer operations of vessels in the port of Galati, the largest port on the Danube River.

Along with an increased interest for connected vehicles and autonomous driving, the Cooperative Intelligent Transportation Systems (C-ITS) are being investigated and validated through the use of C-ITS messages, such as Cooperative Awareness Messages (CAMs). In this paper we demonstrate a tool to support research on CAMs, since C-ITS deploy the Cooperative Awareness Basic Service to exchange CAMs among road C-ITS entities, e.g., vehicles and roadside units (RSUs). These messages provide awareness of traffic information in the Non-line-of-sight (NLOS) of the vehicle (e.g., speed, location, heading), and are an enabler of improving safety in vehicles. Therefore, it is important that those messages are received at the receiving C-ITS vehicle with low latency. In this demo, we showcase how the size of a particular CAM that carries information about the vehicle and surrounding infrastructure affects the latency. In order to demonstrate this effect, we use two leading technologies that support the first generation V2X communication respectively ITS-G5 (IEEE 802.11p) and LTE-V2X (3GGP). We have tested our proposal in a real life C-ITS testbed, at the Smart Highway located in Antwerp, Belgium.

The Cooperative Intelligent Transportation System (C-ITS) testbed or simplified called the Smart Highway (Antwerp, Belgium) is designed to facilitate research in the area of distributed/edge computing and vehicular communications. The Smart Highway testbed deploys the Cooperative Awareness Basic Service to exchange Cooperative Awareness Messages (CAMs) between road C-ITS entities, e.g., C-ITS vehicles and Road-Side Units (RSUs). CAMs support vehicular safety and traffic efficiency applications by providing them with the continuous status information of relevant C-ITS entities. Therefore, it is important that those messages are delivered with low latency, especially the CAMs that originate from special vehicles, e.g., emergency vehicles, police cars, and fire trucks. In this paper, we research the impact of CAM messages configuration on the communication latency among vehicles. Moreover, we have performed the practical experimentation to evaluate the aforementioned impact, using ITSG5 and LTE-V2X system under realistic vehicular conditions, on the Smart Highway testbed located in Antwerp.

With the immense opportunities to make a communication network programmable, the virtualization of network functions and software defined networking are gaining momentum in both industry and research circles, being a fundamental skill-set for both electrical engineers and computer scientists. Therefore, in this article, we present and evaluate the 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, with the goal to bridge the gap between network programmability concepts applied in industry and those taught at the University. We evaluate the educational framework with two extensive surveys as a feedback from students that provided us with the opportunity to measure and quantify students’ experience and satisfaction with the framework. In particular, based on the challenging environment imposed by COVID-19, we identify the gaps in this educational framework and address improvements for both theoretical and practical components according to the students’ needs. Our educational framework and the thorough evaluation serve as a useful guideline on how to modernize the engineering courses and keep up with the pace of technology.

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.