| Short name | Deliverable | Abstract |
|---|---|---|
| 6G-DATADRIVEN-01-E7 | Initial system architecture | This report describes the first overall system architecture to achieve in-network computing and integration of different edge platforms in Industry 4.0 environments. The initial system architecture focusses on the integration of different edge platform using the concept of federation that is performed in an automatic manner using DLT. The initial architecture is presented together with the proposed applicability of DLT for multi-edge federation. In addition, the exiting challenges in performing dynamic federation are presented and some federation scenarios where DLT can be applied. |
| 6G-DATADRIVEN-02-E2 | E1 - Detailed workplan document | This document represents Deliverable E1 – Detailed work plan document (Month 2), within Activity 1 (A1) – 6G-DATADRIVEN-02-E2 (Execution plan of subcontractor 3 in 6G-DATADRIVEN-02) of the contract concluded on July 2023 according to the Award Resolution by the Contracting Authority on May 25, 2023. The document describes the detailed execution plan of the contract, including activities, expected results and management indications, as well as a quality plan. |
| 6G-DATADRIVEN-02-E8 | Draft System Architecture | This document details the initial system design of the architecture for 6G-DATADRIVEN-02, which relies on artificial intelligence to manage the connected industry. The proposed architecture integrates artificial intelligence and data collection tools to automate maintenance and production tasks in connected industry environments. |
| 6G-DATADRIVEN-02-E9 | Revised System Architecture | This document presents the revised system design of the 6G-DATADRIVEN-02 architecture. It updates the main entities and building blocks present in the system architecture to procure automated and zero-touch deployment that leverages AI/ML mechanisms in Industry 4.0 scenarios. The document considers a pool of factory floors connected to a central cloud to perform Industry 4.0 related tasks using AI/ML and an autonomous orchestration loop. This deliverable focuses in specifying the federated AI techniques, data sources, interconnectivity, and process automation. |
| 6G-DATADRIVEN-02-E20 | Initial design of orchestration tools | Industrial scenarios present myriad wireless networking and device capacity challenges. In order to ensure the effective deployment and life-cycle management of network applications for industry 4.0, orchestration tools that are fit for purpose are required. In this document we highlight the most significant of these challenges and how they can be addressed. We then propose a design that leverages the state of the art in compute and network virtualization to realize a robust orchestrator for the connected industry. |
| 6G-DATADRIVEN-03-E8 | Initial system architecture | This document describes the initial draft of the system architecture, enabling Artificial Intelligence services within future Beyond 5G and 6G networks. Furthermore, methods to exploit scenarios with distributed data sources for the training of Machine Learning models are detailed. |
| 6G-DATADRIVEN-04-E6 | Initial system architecture | This document provides an initial version of the system design for 6G-DATADRIVEN-04. The document details the components of the system and explains the required extensions for its implementation. |
| 6G-DATADRIVEN-04-E7 | Revised System Architecture | This report describes the revised system architecture to achieve reliable and deterministic network connectivity in Industry 4.0 environments. The revised system architecture focusses on the multi-technology connectivity layer that is split into multi-domain control plane and multi-domain data plain that will focus on ensuring the E2E service determinism in Industrial environments over multiple technologies. In addition, the role of Digital Twins in each of the technological domains is envisioned to provide predictability. |
| 6G-DATADRIVEN-04-E9 | Document specifying RAW extensions (initial release) | This document provides an analysis of RAW (Reliable and Available Wireless) extensions/solutions needed for industrial environments for the project 6G-DATADRIVEN-04, and also a potential plan for adoption of some of the proposed solutions in the IETF. The document sketches at high-level several of the solutions that will be studied and developed in more detail throughout the project. |
| 6G-DATADRIVEN-04-E10 | RAW extensions: intermediate release | This report includes a set of refined proposed RAW extensions required for industrial scenarios, including a summary of activities at the IETF. |
| 6G-DATADRIVEN-04-E15 | Multi-SDO RAW extensions (initial version) | This document provides the initial specification of RAW (Reliable and Available Wireless) extensions/solutions that integrate mechanisms developed by different standardization organizations (SDOs: Standardization Development Organizations) necessary for industrial environments for the 6G-DATADRIVEN-04 project, as well as a plan of potential adoption of some of the solutions proposed at the IETF. It is based on the motivation and introduction detailed in the deliverable 6G-DATADRIVEN-04-E9. |
| 6G-DATADRIVEN-05-E9 | Distributed orchestration for AI/ML (initial release) | This document reviews the state of the art on distributed orchestration for the 6G-DATADRIVEN-05 project. It also exposes an initial design of a framework enabling agile deployment of AI/ML algorithms on the Edge. |
| 6G-DATADRIVEN-05-E10 | Distributed orchestration for AI/ML (final release) | AI/ML driven orchestration of resources is a key enabler that promises to facilitate efficient operations of the connected industry. We provide a thorough review of current state of the art in distributed orchestration in this deliverable and propose a framework leveraging edge-AI to create smart agile orchestration agents. Our work in this deliverable also provides a basis for the edge-continuum for federated AI. |
| 6G-DATADRIVEN-06-E5 | Architecture for data and AI use in emergencies: state of the art | This document provides an overview of artificial intelligence and machine learning techniques applied to emergency situations. New generation of wireless communications, like WiFi-6 or 5G, also join a crucial role to enable the potential of these techniques on multiple scenarios. The document classifies the types of machine learning techniques and distinguishes three types of emergencies where these tools are applied. |
| 6G-DATADRIVEN-06-E6 | Architecture for data and AI use in emergencies: initial solution | The integration of Artificial Intelligence (AI) and 5G/6G networks in enhancing emergency medical responses has increased in importance in the last years. This document highlights how these advanced technologies can significantly improve response times and efficiency through real-time data processing and AI-driven applications. It discusses the architectural framework for medical emergencies, employing AI and Machine Learning techniques for health monitoring and crisis detection. The document also addresses challenges such as data privacy and network reliability, advocating for ongoing research and cross-sector collaboration. Ultimately, it emphasizes the potential of these technologies in revolutionizing emergency services and improving public safety. |
| 6G-DATADRIVEN-01-E18 6G-DATADRIVEN-02-E28 6G-DATADRIVEN-03-E24 6G-DATADRIVEN-04-E18 6G-DATADRIVEN-05-E17 6G-DATADRIVEN-06-E20 |
Report of communication and dissemination activities (2022) | This document reports on all the achievements and activities undertaken during the execution of the 6G-DATADRIVEN subprojects in 2022 in relation to communication and dissemination. Given that these subprojects are related to each other, it has been decided to integrate the individual reports of each subproject in a single document. |
