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

Illustrated Technical Paper - Enhanced Pub/Sub Network Communication

Illustrated Technical Paper - Enhanced Pub/Sub Network Communication: This illustrated technical paper presents the slides describing the contents of the paper 'Enhanced Pub/Sub Communications for Massive IoT Traffic with SARSA Reinforcement Learning'. The talk was presented at the 3rd International Conference on Machine Learning for Networking (MLN'2020), 24 - 26 November 2020 at Paris, France - http://www.adda-association.org/mln-2020. The illustrated technical paper format is intended to complement, enrich and subsidize the technical paper content and contains slides, complementary text and additional and/or focused bibliographic references.

Enhanced Pub/Sub Communications for Massive IoT Traffic with SARSA Reinforcement Learning

Enhanced Pub/Sub Communications for Massive IoT Traffic with SARSA Reinforcement Learning: Sensors are being extensively deployed and are expected to expand at significant rates in the coming years. They typically generate a large volume of data on the internet of things (IoT) application areas like smart cities, intelligent traffic systems, smart grid, and e-health. Cloud, edge and fog computing are potential and competitive strategies for collecting, processing, and distributing IoT data. However, cloud, edge, and fog-based solutions need to tackle the distribution of a high volume of IoT data efficiently through constrained and limited resource network infrastructures. This paper addresses the issue of conveying a massive volume of IoT data through a network with limited communications resources (bandwidth) using a cognitive communications resource allocation based on Reinforcement Learning (RL) with SARSA algorithm. The proposed network infrastructure (PSIoTRL) uses a Publish/ Subscribe architecture to access massive and highly distributed IoT data. It is demonstrated that the PSIoTRL bandwidth allocation for buffer flushing based on SARSA enhances the IoT aggregator buffer occupation and network link utilization. The PSIoTRL dynamically adapts the IoT aggregator traffic flushing according to the Pub/Sub topic's priority and network constraint requirements.

Smart City Drivers and Challenges in Energy and Water Systems

Smart City Drivers and Challenges in Energy and Water Systems: 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 sys...

Smart City Drivers and Challenges in Urban-Mobility, Health-Care, and Interdependent Infrastructure Systems

Smart City Drivers and Challenges in Urban-Mobility, Health-Care, and Interdependent Infrastructure Systems: 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 sys...