This article introduces the key innovations of the 5Growth service platform to empower verticals industries with an AI-driven automated 5G End-to-End (E2E) slicing solution which allows industries to achieve their service requirements. Specifically, we present multiple vertical pilots (Industry 4.0, Transportation and Energy), identify the key 5G requirements to enable them and analyze existing technical and functional gaps as compared to current solutions. Based on the identified gaps, we propose a set of innovations to address them with: (i) support of 3GPP-based RAN slices by introducing a RAN slicing model and providing automated RAN orchestration and control, (ii) an AI-driven closed-loop for automated service management with Service Level Agreement (SLA) assurance, and, (iii) Multi-domain solutions to expand service offerings by aggregating services and resources from different provider domains and also enable the integration of private 5G networks with public networks.
5G networks require flexibility, automation and programmability to satisfy the requirements of verticals industries. 5G-TRANSFORMER project proposes an SDN/NFV based network platform to enable this vision. Among its features, this platform allows the end-to-end deployment of parts of a network service (NS) in multiple administrative domains, which is known as network service federation (NSF). This feature increases network flexibility, opening the door to new business models. This paper complements our previous work by providing a detailed description of the 5G-TRANSFORMER NSF workflow, its interface and a profiling of the operations involved in the deployment of an NS between multiple administrative domains in a real experimental setup. Experimental results reveal i) that a federated NS can be deployed in the order of few minutes (less than 5 minutes), in line with the 5G target of reducing service setup to minutes, and ii) the impact of the NSF procedure in the deployment time is reduced when compared with the deployment of the same NS in a single administrative domain.
Abstract-Campus libraries in modern universities provide students with group study areas where they can work and study collaboratively. In this paper, we propose a complete solution for the creation of study groups in future smart libraries featuring (i) a smartphone application to create study groups, (ii) a hybrid Bluetooth Low Energy (BLE) and Wi-Fi indoor positioning system to localize study groups and (iii) a server-based infrastructure based on MQTT and Node-RED to advertise study groups to other students. We describe in details all components of the architecture and perform an experimental evaluation of the indoor positioning system in a realistic scenario.
Cloud robotics aims at endowing robot systems with powerful capabilities by leveraging the computing resources available in the Cloud. To that end, the Cloud infrastructure consolidates services and information among the robots, enabling a degree of centralization which has the potential to improve operations. Despite being very promising, Cloud robotics presents two critical issues: (i) it is very hard to control the network between the robots and the Cloud (e.g., long delays, high jitter), and (ii) local context information (e.g., on the access network) is not available in the Cloud. This makes hard to achieve deterministic performance for robotics applications. Over the last few years, Edge computing has emerged as a trend to provide services and computing capabilities directly in the access network. This is so because of the additional benefits enabled by Edge computing: (i) it is easier to control the network end-to-end, and (ii) local context information (e.g., about the wireless channel) can be made available for use by applications.The goal of this paper is to showcase, by means of real-life experimentation, the benefits of residing at the Edge for robotics applications, due to the possibility of consuming context information locally available. In our experimentation, an application running in the Edge controls over a Wi-Fi link the movement of a robot. Information related to the wireless channel is made available via a service at the Edge, which is then consumed by the application.Results show that a smoother driving of the robot can be achieved when wireless quality information is considered as input of the movement control algorithm.
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