• Energy Research

{"references": ["G. N. Dias, J. F. Rezende, L N. Ciuffo, I. Machado, Flavio Silva, Tereza C. de Brito, F. Redigolo, Joberto S. B. Martins, Leobino Sampaio, and Antonio Abelem. SFI2 - Slicing Future Internet Infrastructures project. In Proceedings of the The Global Experimentation for Future Internet, pages 1\u20133, Coimbra, Portugal, November 2019.", "Joberto S B Martins, Tereza C. Carvalho, Flavio Silva, and Rodrigo Moreira. SFI2 Network Slicing Reference Architecture. Technical Report TR03/2022, Universidade de S\u00e3o Paulo - USP, 2022.", "Rodrigo Moreira, Joberto S. B. Martins, Tereza C. M. B. Carvalho, and Fl\u00e1vio de Oliveira Silva. On Enhancing Network Slicing Life-Cycle Through an AI-Native Orchestration Architecture. In Leonard Barolli, editor, Advanced Information Networking and Applications, Lecture Notes in Networks and Systems, pages 124\u2013136, Cham, 2023. Springer International Publishing.", "Joberto S B Martins. Communication Slice Allocation for Network Slicing - Position Points, volume 24 of JSMNet Networking and Technical Review. 2023."]} This project deliverable summarizes the technical discussions and presents the slides and additional decisions and technical information concerning the SFI2 Project Workshop realized in Uberlândia, Minas Gerais. The SFI2 project (https://sites.google.com/view/sfi2/home) workshop presentations and technical discussions occurred at the Federal University of Uberlândia (UFU) on April 12th - 13th 2023.

The 6G mobile network is the next evolutionary step after 5G, with a prediction of an explosive surge in mobile traffic. It provides ultra-low latency, higher data rates, high device density, and ubiquitous coverage, positively impacting services in various areas. Energy saving is a major concern for new systems in the telecommunications sector because all players are expected to reduce their carbon footprints to contribute to mitigating climate change. Network slicing is a fundamental enabler for 6G/5G mobile networks and various other new systems, such as the Internet of Things (IoT), Internet of Vehicles (IoV), and Industrial IoT (IIoT). However, energy-saving methods embedded in network slicing architectures are still a research gap. This paper discusses how to embed energy-saving methods in network-slicing architectures that are a fundamental enabler for nearly all new innovative systems being deployed worldwide. This paper's main contribution is a proposal to save energy in network slicing. That is achieved by deploying ML-native agents in NS architectures to dynamically orchestrate and optimize resources based on user demands. The SFI2 network slicing reference architecture is the concrete use case scenario in which contrastive learning improves energy saving for resource allocation. 10 pages, 7 figures, International Workshop on ADVANCEs in ICT Infrastructures and Services, 2025

The IEEE Smart Cities Initiative brings together the IEEE’s broad array of technical societies and organizations to advance the state of the art for smart city technologies for the benefit of society and to set the global standard in this regard by serving as a neutral broker of information amongst industry, academic, and government stakeholders. These smart city technologies draw upon expertise in several functional domains including: ● Sensors and Intelligent Electronic Devices ● Communication Networks & Cyber Security ● Systems Integration ● Intelligence & Data Analytics ● Management & Control Platforms Together, this functional expertise serves to achieve the mission of the IEEE Smart Cities initiative: 1. To be recognized as the authoritative voice and leading source of credible technical information and educational content within the scope of smart cities identified below. 2. To facilitate and promote both the collaborative and individual work of its Member societies regarding smart city technology. To that end, the IEEE Smart Cities initiative has identified several application domains in which to apply its expertise. These are: ● Smart energy systems ● Smart water systems ● Smart mobility systems ● Smart healthcare systems Each of these systems has generally developed in its own right in response to the needs and context of the domain. Each faces its own set of drivers and challenges. And yet, as each of these systems gains greater “digital intelligence”, recurring themes of technology integration do emerge. This sequence of two articles serves to highlight these domain-specific drivers and challenges within the broader smart city landscape. This first article focuses on smart energy and smart water systems. In the sequel article, smart mobility and healthcare systems are discussed. {"references": ["S. O. Muhanji, A. E. Flint and A. M. Farid, EIoT: The Development of the Energy Internet of Things in Energy Infrastructure, Berlin:Springer-Verlag, 2019.", "W. C. Schoonenberg, I. S. Khayal and A. M. Farid, A Hetero-functional Graph Theory for Modeling Interdependent Smart City Infrastructure, Berlin:Springer-Verlag, 2018.", "Y. Yih, Handbook of Healthcare Delivery Systems, Boca Raton, FL:CRC Press, 2016.", "S. Shaheen, H. Totte and A. Stocker, Future of mobility white paper, 2018, [online] Available: https://escholarship.org/uc/item/68g2h1qv#author.", "P. Tsakalides, A. Panousopoulou, G. Tsagkatakis and L. Montestruque, Smart Water Grids: A Cyber-Physical Systems Approach, Boca Raton, FL:CRC Press, 2018.", "Intelligent water systems: The path to a smart utility, 2017."]}

{"references": ["Antonio Abelem, Michael Stanton, Iara Machado, Marcos Salvador, Luiz Magalhaes, Natalia Fernandes, Sand Correa, Kleber Cardoso, Cesar Marcondes, Joberto Martins, Jose Monteiro, Tereza Carvalho, and Jos\u00e9 Rezende. FIT@ BR - A Future Internet Testbed in Brazil. In Proceedings of the APAN \u2013 Network Research Workshop, pages 1\u20138, 2013.", "Aloizio P. Silva, Christos Tranoris, Spyros Denazis, Susana Sargento, Jo\u00e3o Pereira, Miguel Lu\u00eds, Rodrigo Moreira, Fl\u00e1vio Silva, Ivan Vidal, Borja Nogales, Reza Nejabati, and Dimitra Simeonidou. 5GInfire: An End-to-End Open5G Vertical Network Function Ecosystem. Ad Hoc Networks, 93:101895, October 2019.", "Cristiano Both, Rafael Guimaraes, Frank Slyne, Juliano Wickboldt, Magnos Martinello, Cristina Dominicini, Rafael Martins, Yi Zhang, Diego Cardoso, Rodolfo Villaca, Isabella Ceravolo, Reza Nejabati, Johann Marquez-Barja, Marco Ruffini, and Luiz DaSilva. FUTEBOL Control Framework: Enabling Experimentation in Convergent Optical, Wireless, and Cloud Infrastructures. IEEE Communications Magazine, 57(10):56\u201362, October 2019.", "Stuart Clayman, Augusto Neto, F\u00e1bio Verdi, Sand Correa, Silvio Sampaio, Ilias Sakelariou, Lefteris Mamatas, Rafael Pasquini, Kleber Cardoso, Francesco Tusa, Christian Rothenberg, and Joan Serrat. The NECOS Approach to End-to-End Cloud-Network Slicing as a Service. IEEE Communications Magazine, 59(3):91\u201397, March 2021. Conference Name: IEEE Communications Magazine.", "Gustavo Neves Dias, Jos\u00e9 Ferreira Rezende, Leandro Neumann Ciuffo, Iara Machado, Flavio de Oliveira Silva, Tereza Cristina de Brito, Fernando Frota Redigolo, Joberto S. B. Martins, Leobino N. Sampaio, and Antonio Jorge Gomes Abelem. SFI2 - Slicing Future Internet Infrastructures project. In Proceedings of the The Global Experimentation for Future Internet (GEFI), pages 1\u20133, Coimbra, Portugal, November 2019.", "Fl\u00e1vio de Oliveira Silva, Tereza Cristina Carvalho, Joberto S. B. Martins, Cristiano Bonato Both, and Daniel Fernandes Macedo. Slicing Future Internet Infrastructures Round Table. Technical Report TR01/2021, Universidade de S\u00e3o Paulo (USP), S\u00e3o Paulo, Brazil, 2021."]} The future Internet and computer networks scenario requires a dynamic, on-demand, easily manageable, intelligent, and flexible network capable of supporting the different requirements of current and future applications. For attaining this objective, the research community is turning to the networks of the future involving initiatives such as the Next Generation Internet (NGI) in Europe and Tomorrow’s Internet Project in the United States, in addition to several other actions such as the next generation of mobile telecommunications networks for 2030, 6G. In Brazil, projects such as FIBRE [1], FIWARE [2], FUTEBOL [3], 5GINFIRE[2], NECOS [4], CloudNEXT, and SFI2 [5] [6], carried out in collaborations between Europe and Brazil, create conditions to experiment with different scenarios of networks and applications with a focus on the future. This SFI2 Workshop aims to discuss, exchange research ideas and propose directions and solutions toward the utilization of network slicing (NS) as a new paradigm capable to support future Internet evolution. SFI2 will focus on experimental network integration as a guidance direction for these discussions and proposals.

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Among the challenges for the development of new generation network for smart grids, it is possible to highlight security, monitoring, management, control, quality of service and new technology updates. This paper focuses on investigating mechanisms for management and monitoring with ubiquitous and autonomic features aimed to better support smart grid solutions.

As cidades estão evoluindo no sentido de se tornarem mais eficientes visando a melhoria da qualidade de vida dos cidadãos e a eficiência da operação e dos serviços urbanos respeitando e preservando os aspectos econômicos, sociais e ambientais. Esse desafio de concepção e desenvolvimento de um novo cenário urbano, comumente denominado de cidade inteligente (smart city), considera a inovação, a tecnologia e a governança de forma articulada e integrada. Uma cidade inteligente redefine a governança urbana visando desenvolver novas abordagens que possam tornar as cidades mais eficientes, humanas e sustentáveis. O projeto InovaCID (Cidade Inteligente com Inovação e Sustentabilidade e Inteligência Artificial") propõe uma cidade inteligente que inova, observa a sustentabilidade de seus projetos e faz uso da inteligência artificial (IA) como habilitador dos seus projetos visando a qualidade de vida do cidadão. Cidades inteligentes com inteligência artificial são uma inovação que aborda os problemas de desenvolvimento urbano e promove a otimização desses. O projeto InovaCID suporta a transformação digital das cidades focando especificamente nas área de saúde (e-health), desenvolvimento urbano e eficiência energética. No projeto InovaCID, a utilização da IA permite que aspectos dos centros urbanos difíceis de tratar até então como o grande volume de dados, os requisitos heterogêneos e os múltiplos objetivos inerentes à um contexto de governança urbana sejam atendidos de forma inovadora e eficiente. Cities are evolving towards becoming more efficient, aiming to improve the quality of life of citizens and the efficiency of operations and urban services, as well as respect and preserve economic, social, and environmental aspects. This challenge of designing and developing a new urban scenario, commonly called a smart city, considers innovation, technology, and governance articulated and integrated. A smart city redefines urban governance by developing new approaches to make cities more efficient, humane, and sustainable. The InovaCID (Smart City with Innovation and Sustainability and Artificial Intelligence) project proposes a smart city that innovates, observes the sustainability of its projects, and uses artificial intelligence (AI) as an enabler of its projects aimed at improving citizens' quality of life. Smart cities with artificial intelligence are an innovation that addresses urban development problems and promotes their optimization. The InovaCID project supports the digital transformation of cities, focusing specifically on health, urban development, and energy efficiency. In the InovaCID project, AI allows aspects of urban centers that were previously difficult to deal with, such as the large volume of data, the heterogeneous requirements, and the multiple objectives inherent to an urban governance context, to be met innovatively and efficiently.

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Proceeding of the 33rd IEEE Southeastern Symposium on System Theory, pp. 117-121, 2001."]} The power network reconfiguration algorithm with an "R" modeling approach evaluates its behavior in computing new reconfiguration topologies for the power grid in the context of the Smart Grid. The power distribution network modelling with the "R" language is used to represent the network and support computation of different algorithm configurations for the evaluation of new reconfiguration topologies. This work presents a reconfiguration solution of distribution networks, with a construction of an algorithm that receiving the network configuration data and the nodal measurements and from these data build a radial network, after this and using a branch exchange algorithm And verifying the best configuration of the network through artificial intelligence, so that there are no unnecessary changes during the operation, and applied an algorithm that analyses the load levels, to suggest changes in the network. CIGRÉ-BRASIL - Brazilian National Committee