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.

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.

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.

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.

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.