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After COVID-19, tuberculosis (TB) is the leading cause of death by an infectious disease in the world. This work presents a data set based on data collected from the Brazilian Information System for Notifiable Diseases (SINAN) for the period from January 2001 to April 2020 relating to patients diagnosed with tuberculosis in Brazil. The data from SINAN was pre-processed to generate a new data set with two distinct treatment outcome classes: CURED and DIED. The data set comprises 37 categorical attributes (including socio-demographic, clinical, and laboratory data) as well as the target class. There are 927,909 records of patients classified as CURED and 36,190 classified as DIED, totaling 964,099 records.

It is expected that the next generation cellular networks provide a connected society with fully mobility to empower the socio-economic transformation. Several other technologies will benefits of this evolution, such as Internet of Things, smart cities, smart agriculture, vehicular networks, healthcare applications, and so on. Each of these scenarios presents specific requirements and demands different network configurations. To deal with this heterogeneity, virtualization technology is key technology. Indeed, the network function virtualization (NFV) paradigm provides flexibility for the network manager, allocating resources according to the demand, and reduces acquisition and operational costs. In addition, it is possible to specify an ordered set of network virtual functions (VNFs) for a given service, which is called as service function chain (SFC). However, besides the advantages from service virtualization, it is expected that network performance and availability do not be affected by its usage. In this paper, we propose the use of reinforcement learning to deploy a SFC of cellular network service and manage the VNFs operation. We consider that the SFC is deployed by the reinforcement learning agent considering a scenarios with distributed data centers, where the VNFs are deployed in virtual machines in commodity servers. The NFV management is related to create, delete, and restart the VNFs. The main purpose is to reduce the number of lost packets taking into account the energy consumption of the servers. We use the Proximal Policy Optimization (PPO) algorithm to implement the agent and preliminary results show that the agent is able to allocate the SFC and manage the VNFs, reducing the number of lost packets., Comment: 9 pages, 6 figures, 2 tables

Software defined networking (SDN) and network functions virtualisation (NFV) are making networks programmable and consequently much more flexible and agile. To meet service level agreements, achieve greater utilisation of legacy networks, faster service deployment, and reduce expenditure, telecommunications operators are deploying increasingly complex service function chains (SFCs). Notwithstanding the benefits of SFCs, increasing heterogeneity and dynamism from the cloud to the edge introduces significant SFC placement challenges, not least adding or removing network functions while maintaining availability, quality of service, and minimising cost. In this paper, an availability- and energy-aware solution based on reinforcement learning (RL) is proposed for dynamic SFC placement. Two policy-aware RL algorithms, Advantage Actor-Critic (A2C) and Proximal Policy Optimisation (PPO2), are compared using simulations of a ground truth network topology based on the Rede Nacional de Ensino e Pesquisa (RNP) Network, Brazil's National Teaching and Research Network backbone. The simulation results showed that PPO2 generally outperformed A2C and a greedy approach both in terms of acceptance rate and energy consumption. A2C outperformed PPO2 only in the scenario where network servers had a greater number of computing resources.

CAPES The Internet of Things has the potential of transforming health systems through the collection and analysis of patient physiological data via wearable devices and sensor networks. Such systems can offer assisted living services in real-time and offer a range of multimedia-based health services. However, service downtime, particularly in the case of emergencies, can lead to adverse outcomes and in the worst case, to death. In this dissertation, we propose an e-health monitoring architecture based on sensors that relies on cloud and fog infrastructures to handle and store patient data. Furthermore, we propose stochastic models to analyze availability and performance of such systems including models to understand how failures across the Cloud-to-Thing continuum impact on ehealth system availability and to identify potential bottlenecks. To feed our models with real data, we designed and built a prototype and executed performance experiments. Our results identify that the sensors and fog devices are the components that have the most significant impact on the availability of the e-health monitoring system, as a whole, in the scenarios analyzed. Our findings suggest that in order to identify the best architecture to host the e-health monitoring system, there is a trade-off between performance and delays that must be resolved. A Internet das Coisas tem o potencial de transformar sistemas e-health através da coleta e análise de dados fisiológicos do paciente através de dispositivos vestíveis e redes de sensores. Tais sistemas podem oferecer serviços de monitoramento em tempo real e oferecer serviços de saúde baseados em multimídia. No entanto, o tempo de inatividade do serviço, particularmente no caso de emergências, pode levar a resultados adversos e, no pior dos casos, à morte. Nesta dissertação, foi proposta uma arquitetura de monitoramento de e-health baseada em sensores que dependem de infra-estruturas de cloud e fog para manipular e armazenar dados de pacientes. Além disso, modelos estocásticos foram propostos para analisar a disponibilidade e o desempenho de tais sistemas, incluindo modelos para entender como as falhas desde os dispositivos edge até a nuvem afetam a disponibilidade do sistema e-health e para identificar possíveis gargalos. Para alimentar os modelos com dados reais, um protótipo foi projetado e construído com o intuito de executar experimentos acerca do desempenho do sistema e-health. A partir dos resultados, é possível identificar que os sensores e dispositivos fog são, de maneira geral, os componentes que mais impactam na disponibilidade do sistema e-health nos cenários analisados. Além disso, é possível concluir que para identificar a melhor arquitetura para hospedar um sistema e-health, é necessário encontrar um equilíbrio entre o desempenho e a latência.