The consolidation of the Internet as a global and pervasive communication network has fostered the emergence of a connected global society, generating a massive amount of data. The Internet is, in fact, an interconnection of heterogeneous networks, flexible enough to allow the deployment of new applications. On the other hand, the dissemination of sensors in a wide range of everyday devices enables the construction of intelligent systems, such as smart cities, which have the potential to promote greater social well-being as well as foster the economy.
The emergence of Artificial Intelligence (AI) as a fundamental area for solving complex problems has led to its adoption in various systems, such as the Internet of Things (IoT) and communication networks. The processing of raw data generated by sensors has the potential to generate information, knowledge, and added value. The multiplicity and diversity of information in the operation of communication networks demand solutions that can only be derived from the use of AI.
Currently, the execution of machine learning algorithms is typically batch, offline and centralized. Network management and its services require massive execution of distributed and real-time data. In many situations, the real-time validity of the generated data is limited, requiring the reduction of latency in communication and processing. Furthermore, data transmission in a distributed environment is subject to the quality of communication channels, network congestion and available energy in mobile devices. Such restrictions require solutions based on Distributed Artificial Intelligence (DAI), which goes far beyond the traditional execution of machine learning algorithms. An additional strong restriction arises from the adoption of the new General Data Protection Law – LGPD. The data privacy restriction is addressed by the federated learning technique. The large number of devices connected to the Internet of Things requires the handling of a high volume of data generated by thousands of sensors, requiring solutions that meet scalability, geographic distribution, mobility, heterogeneity, security and privacy requirements.
The expected massive growth of mobile Internet traffic in 5G mobile networks introduces the need to change the operators’ networks. Such networks require a drastic transformation towards open, scalable and elastic ecosystems supporting new types of communication. The PORVIR-5G project will develop and demonstrate a programmable fronthaul and backhaul integrating wireless with optical-packet networks and cloud solutions. It is intended to exploit virtual network splits that optimise resource allocation across the wireless, optical, packet, and compute/storage domains. Key enablers for PORVIR-5G are (i) Slicing over packet, wireless, and optical resources, controlled by (ii) deep programmability interfaces, where the devices are configured by network functions to provide the required performance for the future applications on the Internet. This programmability allows a more refined (iii) end-to-end and multilayer orchestration, considering the quality of experience of the users for each type of applications over the network. This project will validate and demonstrate the proposed programmability and virtualization capabilities in three demonstrations, each one of them enabling the key performance demands of 5G networks: an Internet of Things demonstrator focusing on massive machine-type communication; a smart city demonstration for reliable and ultra-low latency flows; and a high bandwidth video demonstrator showcasing the next-generation mobile broadband.
PeTWIN will define a research-based vision and best practices for implementing sustainable, usable and maintainable digital twins for field management. The work addresses the substantial IT challenges posed by digital twins through the application and development of knowledge representation techniques and data analytics. The focus of the project is to build the knowledge and competence needed to build the next generation of digital twins for field digitalization.
The overall objective of the FUTEBOL project is to develop and deploy research infrastructure, and an associated control framework for experimentation, in Europe and Brazil, that enables experimental research at the convergence point between optical and wireless networks.