Dynamic Map-based Data-Centric Approach for Tourism and Cultural Heritage Preservation Digital Twins - Université d'Évry

Dynamic Map-based Data-Centric Approach for Tourism and Cultural Heritage Preservation Digital Twins - Université d'Évry: Tourism is an essential and growing economic activity worldwide, bringing benefits such as job creation, revenue generation, and tax revenue, and driving economic prosperity. Tourism activity may also have negative impacts on cities, such as overtourism and pressure on housing and real estate, which are increasingly recognized as problems communities must address. Cultural heritage is an essential asset of cities and countries that must be preserved. Cultural heritage, as an asset, is commonly explored through tourism activities and may have negative impacts, including physical degradation, commodification, and loss of authenticity, among others. Tourism and cultural heritage management are common elements of smart city digital transformation strategies, in which the well-being of citizens and the maintenance of public assets are among the goals. A digital twin is a data-driven virtual representation of a physical object, system, or environment. It typically integrates real-time data and computational models to simulate, monitor, analyze, and predict the behavior and performance of the represented entity. However, although digital twin technology has been widely adopted in manufact

Towards Sustainability in 6G Network Slicing with Energy-Saving and Optimization Methods - Archive ouverte HAL

Towards Sustainability in 6G Network Slicing with Energy-Saving and Optimization Methods - Archive ouverte HAL: 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

Intelligent Data-Driven Architectural Features Orchestration for Network Slicing

Intelligent Data-Driven Architectural Features Orchestration for Network Slicing:

Network slicing is a crucial enabler and a trend for the Next Generation Mobile Network (NGMN) and various other new systems like the Internet of Vehicle...

Communication Slice Modeling and Optimization with SARSA Reinforcement Learning

Communication Slice Modeling and Optimization with SARSA Reinforcement Learning: In this document, we present a conceptual model of network slicing, we then formulate analytically some aspects of the model and the optimization problem to address. Next, we propose to use a reinforcement learning SARSA agent to solve the optimization problem and implement a proof of concept prototype highlighting its results.

Modeling and Accomplishing the BEREC Network Neutrality Policy

Modeling and Accomplishing the BEREC Network Neutrality Policy

Future Internet and Smart City with OpenFlow and Machine Learning - Course Poster

Future Internet and Smart City with OpenFlow and Machine Learning - Course Poster

Reconfiguração de Redes de Distribuição de Energia Elétrica Utilizando Aprendizado de Máquina

Reconfiguração de Redes de Distribuição de Energia Elétrica Utilizando Aprendizado de Máquina: Electrical networks are composed of stages of generation, transmission, and distribution of energy. Distribution networks (RD) are an important element of the electricity grid because it provides the effective delivery of energy to end users. The RD’s are subject to failure and their optimization is of fundamental importance in the context of Smart Grids, where it is sought a greater efficiency of the processes involved between the production and distribution of energy. The distribution networks (RD) have topologies and loads of various type. This dissertation proposes a method and algorithm for the reconfiguration of electrical network using machine learning with linear regression and branch exchange algorithm aiming the optimization of RD operation. The method and algorithm proposed do maneuvers in the RD as transfer and load balancing and aiming to increase its level of reliability. The proposal is validated in a test network of IEEE (IEEEbus14) using simulation and testing environment implemented in “R” language and using the Newton Raphson method to calculate the power flow. The solution developed show satisfactory in supporting the decision-making for three reconfigurations of distribution networks in the context of the Smart Grid.

COGCOM Project -- Cognitive Allocation of Network Communication Resources for Edge/Fog/Cloud Computing

COGCOM Project -- Cognitive Allocation of Network Communication Resources for Edge/Fog/Cloud Computing

BAMSim Simulator

BAMSim Simulator

Uma Proposta para a Gerência Autonômica e Escalável de Redes de Computadores

Uma Proposta para a Gerência Autonômica e Escalável de Redes de Computadores