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

Network function virtualization and edge computing in mobile networks are key enablers in the evolution of recent standards for a 5 Generation (5G) mobile communication system towards a comprehensive 5G ecosystem, which enables the deployment of customized networks and service access for various industry verticals by means of clearly defined and deployed network slices. In particular, the automotive industry can leverage low-latency access to services hosted along the distributed network edge, in support of a large variety of use cases. Network slices typically implement the requested service and network according to a set of defined requirements, as well as performance and latency bounds, while using a defined resources budget. With connected cars following different mobility patterns, the automotive sector represents a pretty agile customer of such network slices. This makes the management of network and edge computing resources a key challenge to tackle in order to balance the resource utilization, and the previously agreed and expected service levels. In this paper, we analyze the benefit of smart mobile edges and the use of machine learning to anticipate resource demand at distributed mobile edges in an automotive scenario. Finally, we experimentally show the feasibility to treat μ-slice resources efficiently by using an OSS-based prototype for the orchestrated edges, which is currently being developed for an automotive trial in Europe.

5G has opened up possibilities of introducing new use cases and business models that could not be perceived before. In the context of public safety, 5G offers immense opportunities towards enhancing mission success and situation awareness during emergency management. This paper introduces Back-Situation Awareness (BSA) application enabling early warning/notification to vehicles of an approaching emergency vehicle indicating its presence and the time it will arrive. Such an application is expected to give drivers enough time to create a safety corridor for the emergency vehicle to pass through safely and unhindered. We provide details on the system and application design of the BSA application leveraging Multi-Access Edge Computing (MEC) systems that complement the 5G mobile communication system. An evaluation of the application is provided by using data measurements and indicating the accuracy of the computation and notification of the Estimated Time of Arrival (ETA) based on the ETSI C-ITS protocol messages.

The concept of Massive Open Online Course (MOOC) brings the opportunity to adjust both the study content, and the context, based on the teaching needs. Therefore, in this paper, we present our best practices on enabling remote networking laboratories via Blackboard platform, including the Blackboard Collaborate Ultra extension, in order to efficiently react to the challenges of imminent campus closure imposed by COVID-19 breakout. We present an extensive survey as a feedback from students, which allowed us to measure and to quantify students’ experience and satisfaction with the remote teaching setup that successfully served 45 enrolled students. As the results bring the positive attitude towards practices presented in this paper, such teaching practices will foster some of the critical skills nowadays, such as collaboration, self-driven learning, and problem solving, and they can also serve as a successful example on how to efficiently cope with the limited access to traditional classroom resources within various courses.

A practical compound of education in computer science and electrical engineering, driven by increased availability and maturity of many emerging technologies, should be enriched by various laboratory resources in order to synchronize the paces between technology advancements and education. In particular, advancements in containerization as a virtualization technique pave the way towards allowing students to deploy their project applications with a lightweight resource footprint on top of the cloud. Being backed by a valuable feedback from 45 Bachelor students, in this paper we present the best practices on how virtualization can be leveraged to create a scalable environment for on-demand remote experimentation with distributed systems.

Cellular technologies are widely used in the ICT domain, that paving the way towards new opportunities for education and practical experimentation. It also tackles software-related engineering areas, from Digital Processing (DSP) to Software Defined Networks and Network Virtualization applications. Due to the various constraints such as limited access to hardware components, high costs of such equipment, and spectrum regulations that must not be violated, practical education in telecommunications, electrical engineering, and electronics usually lacks the opportunity to pursue experimentation on the realistic cellular deployments. Therefore, in this paper we present a network testbed that allows students to experiment with a fully functional 4G LTE system with no radio. This testbed system mimics the realistic 4G LTE deployment, supporting students towards acquiring valuable knowledge in the field of cellular networks. It is low-cost due the fact there is no need for radio components or Software Defined Radio (SDR) devices, with no limitation on frequency utilization and regulations. The testbed provides seamless scalability for education classes as it can be deployed on top of any machine with general-purpose processor, installing the whole system within two networked PCs or in a fully virtualized environment. We present the testbed framework, as well as the hands-on practices on incorporating such low-cost realistic testbed into the education within various engineering fields.