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.

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.

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.

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.

In this paper, we study and present a management and orchestration framework for vehicular communications, which enables service continuity for the vehicle via an optimized application-context relocation approach. To optimize the transfer of the application-context for Connected and Automated Mobility (CAM) services, our MEC orchestrator performs prediction of resource availability in the edge infrastructure based on the Long Short-Term Memory (LSTM) model, and it makes a final decision on relocation by calculating the outcome of a Multi-Criteria Decision-Making (MCDM) algorithm, taking into account the i) resource prediction, ii) latency and bandwidth on the communication links, and iii) geographical locations of the vehicle and edge hosts in the network infrastructure. Furthermore, we have built a proof-of-concept for the orchestration framework in a real-life distributed testbed environment, to showcase the efficiency in optimizing the edge host selection and application context relocation towards achieving continuity of a service that informs vehicle about the driving conditions on the road.

Due to the dynamic nature of changes in various ICT technologies nowadays, the gaps between industry, research, and academia need to be bridged in order to adequately support STEM students towards their future career paths. With the COVID-19 pandemic, and restrictions on access to university premises, an agile transition of both teaching and experimentation was essential, and adjustments in the curriculum were needed more than ever. Therefore, in this paper we present an adaptive and on-demand education framework for engineering students, thereby enabling remote experimentation and adjustments of exercise content to enhance students' learning experience. We present the two types of practical experimentation environments, i.e., cloud and real-life net-working testbed, for performing remote laboratory exercises, as well as the assessment of students' experience that is used as an input for the dynamic adjustments of the exercise content. Our results show that students consider they significantly improved the baseline skills our courses tend to build and strengthen towards preparing students for their future jobs.

The adoption of the 5G technology is becoming a must for the mobile network operators (MNOs) in order to remain competitive in the market and efficiently cope with the increase in the mobile data traffic while providing support for a number of futuristic use cases and services. Given the unprecedented demand of mobile data traffic and its variation, it is of paramount importance to dynamically scale the 5G core network functions as well as the applications, both of which are expected to be deployed as virtual network functions (VNFs), in order to avoid over/under-utilization of these VNFs. In this work, we study the problem of a joint user association, service functions chain (SFC) placement, and VNF scaling with a particular emphasis on analyzing the trade-offs between the VNF scaling strategies. Specifically, we compare vertical, horizontal, and hybrid VNF scaling strategies by formulating an integer linear programming (ILP) problem that aims at minimizing the service provisioning cost for the MNOs, while satisfying users’ data rate requirements. The users’ service requests are represented as SFCs composed of end-to-end mobile network components (e.g., gNBs, 5G core network VNFs, and application VNFs). Finally, we devise a heuristic algorithm to tackle the scalability issue of the ILP-based approach.

In the context of public safety, 5G offers great opportunities towards enhancing mission-critical services, by running network functions at the network edge to provide reliable and low-latency services. This demo introduces an on-demand Back Situation Awareness (BSA) application service, in a multi-domain scenario, enabling early notification for vehicles of an approaching Emergency Vehicle (EmV), indicating its Estimated Time of Arrival (ETA). The application provides the drivers ample time to create a safety corridor for the EmV to pass through unhindered in a safe manner thereby increasing the mission success. For this demo, we have developed an orchestrated MEC platform on which we have implemented the BSA service following modern cloud-native principles, based on Docker and Kubernetes.

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.

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.

NOVEL POWER EFFICIENT CP BLOCK SELECTION PROCEDURE FOR DELAY-TOLERANT SERVICES TO HANDLE CP OUTAGES IN 5G AND BEYOND IN NETWORKS WITH HYBRID TERAHERTZ, MILLIMETER WAVE, AND MICROWAVE MACHINE-LEARNING DIRECTED ARTICLE DETECTION ON THE WEB USING DOM AND TEXT-BASED FEATURES