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At present, modern society is experiencing a significant transformation. Thanks to the digitization of society and manufacturing, mainly because of a combination of technologies, such as the Internet of Things, cloud computing, machine learning, smart cyber-physical systems, etc., which are making the smart factory and Industry 4.0 a reality. Currently, most of the intelligence of smart cyber-physical systems is implemented in software. For this reason, in this work, we focused on the artificial intelligence software design of this technology, one of the most complex and critical. This research aimed to study and compare the performance of a multilayer perceptron artificial neural network designed for solving the problem of character recognition in three implementation technologies: personal computers, cloud computing environments, and smart cyber-physical systems. After training and testing the multilayer perceptron, training time and accuracy tests showed each technology has particular characteristics and performance. Nevertheless, the three technologies have a similar performance of 97% accuracy, despite a difference in the training time. The results show that the artificial intelligence embedded in fog technology is a promising alternative for developing smart cyber-physical systems.

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The growing number of connected objects has allowed the development of new applications in different areas. In addition, the technologies that support these applications, such as cloud and fog computing, face challenges in providing the necessary resources to process information for different applications due to the highly dynamic nature of these networks and the many heterogeneous devices involved. This article reviews the existing literature on one of these challenges: resource allocation in the fog-cloud continuum, including approaches that consider different strategies and network characteristics. We also discuss the factors influencing resource allocation decisions, such as energy consumption, latency, monetary cost, or network usage. Finally, we identify the open research challenges and highlight potential future directions. This survey article aims to serve as a valuable reference for researchers and practitioners interested in the field of edge computing and resource allocation.

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Cloud storage has become a keystone for organizations to manage large volumes of data produced by sensors at the edge as well as information produced by deep and machine learning applications. Nevertheless, the latency produced by geographic distributed systems deployed on any of the edge, the fog, or the cloud, leads to delays that are observed by end-users in the form of high response times. In this paper, we present an efficient scheme for the management and storage of Internet of Thing (IoT) data in edge-fog-cloud environments. In our proposal, entities called data containers are coupled, in a logical manner, with nano/microservices deployed on any of the edge, the fog, or the cloud. The data containers implement a hierarchical cache file system including storage levels such as in-memory, file system, and cloud services for transparently managing the input/output data operations produced by nano/microservices (e.g., a sensor hub collecting data from sensors at the edge or machine learning applications processing data at the edge). Data containers are interconnected through a secure and efficient content delivery network, which transparently and automatically performs the continuous delivery of data through the edge-fog-cloud. A prototype of our proposed scheme was implemented and evaluated in a case study based on the management of electrocardiogram sensor data. The obtained results reveal the suitability and efficiency of the proposed scheme.

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Cloud computing has been widely adopted over the years by practitioners and companies with a variety of requirements. With a strong economic appeal, cloud computing makes possible the idea of computing as a utility, in which computing resources can be consumed and paid for with the same convenience as electricity. One of the main characteristics of cloud as a service is elasticity supported by auto-scaling capabilities. The auto-scaling cloud mechanism allows adjusting resources to meet multiple demands dynamically. The elasticity service is best represented in critical web trading and transaction systems that must satisfy a certain service level agreement (SLA), such as maximum response time limits for different types of inbound requests. Nevertheless, existing cloud infrastructures maintained by different cloud enterprises often offer different cloud service costs for equivalent SLAs upon several factors. The factors might be contract types, VM types, auto-scaling configuration parameters, and incoming workload demand. Identifying a combination of parameters that results in SLA compliance directly in the system is often sophisticated, while the manual analysis is prone to errors due to the huge number of possibilities. This paper proposes the modeling of auto-scaling mechanisms in a typical cloud infrastructure using a stochastic Petri net (SPN) and the employment of a well-established adaptive search metaheuristic (GRASP) to discover critical trade-offs between performance and cost in cloud services.The proposed SPN models enable cloud designers to estimate the metrics of cloud services in accordance with each required SLA such as the best configuration, cost, system response time, and throughput.The auto-scaling SPN model was extensively validated with 95% confidence against a real test-bed scenario with 18.000 samples. A case-study of cloud services was used to investigate the viability of this method and to evaluate the adoptability of the proposed auto-scaling model in practice. On the other hand, the proposed optimization algorithm enables the identification of economic system configuration and parameterization to satisfy required SLA and budget constraints. The adoption of the metaheuristic GRASP approach and the modeling of auto-scaling mechanisms in this work can help search for the optimized-quality solution and operational management for cloud services in practice.

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La internet de las cosas ha mantenido un crecimiento continuo en los últimos años. Las potencialidades de uso que muestra en diferentes campos han sido ampliamente documentadas. Su utilización efectiva en el campo de la salud puede traer consigo mejoras en la eficiencia de los tratamientos médicos, prevenir situaciones de riesgo, ayudar a elevar la calidad del servicio y proporcionar soporte a la toma de decisiones. La presente revisión profundiza en aspectos medulares de su utilización con el objetivo de explorar las principales tendencias y desafíos relacionados con la creciente utilización de la internet de las cosas en la salud, prestando mayor atención a los aspectos relacionados con las arquitecturas utilizadas para el despliegue de sistemas de internet de las cosas en ese ámbito, el manejo de la seguridad de estos sistemas y las herramientas para el apoyo a la toma de decisiones empleadas. Mediante el análisis documental se logra mostrar las principales características de estos sistemas, así como su arquitectura, herramientas utilizadas para la gestión de los datos capturados y mecanismos de seguridad. La utilización de la internet de las cosas en el campo de la salud tiene gran impacto, mejorando la vida de millones de personas en todo el mundo y brindando grandes oportunidades para el desarrollo de sistemas inteligentes de salud(AU)

The internet of things has maintained continuous growth in recent years. The potentialities of use that it shows in different fields have been widely documented. Its effective use in the field of health can bring improvements in the efficiency of medical treatments, prevention of risky situations, help raising the quality of service and provide support for decision-making. The present review explores into core aspects of its use in order to analyze trends, challenges and strengths. Document analysis was used to show the main characteristics of these systems, as well as their architecture, tools used for the management of the captured data and security mechanisms. The use of the internet of things in the health field has a great impact, improving the lives of millions of people around the world and providing great opportunities for the development of intelligent health systems(AU)

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La actual propuesta metodológica se enmarca en los dos primeros objetivos del Plan Nacional de la Lucha contra el Dengue, que son promover, coordinar y facilitar la implementación de estrategias eficaces y oportunas para el control y tratamiento del dengue, con especial énfasis en las zonas geográficas del país que presentan alto riesgo potencial epidémico de dicha enfermedad, y gestionar la cooperación técnica destinada al control y manejo del dengue. En la fase inicial, se determinaron las actividades y variables sensibles a monitoreo, en base a los objetivos e indicadores identificados dentro del marco técnico y normativo vigente en el Perú y el marco sanitario establecido por la OMS, correspondientes a la vigilancia, prevención y control, pero, además, a la promoción de prácticas saludables. Luego, se definieron los requerimientos no funcionales de la plataforma de monitoreo para el desarrollo de un aplicativo accesible a través de un navegador en cualquier dispositivo con acceso a internet, a ser alojado en la plataforma de Cloud Computing. Posteriomente, se realizó el flujograma para el monitoreo de la clasificación de escenarios epidemiológicos y estratificación de riesgo entomológico de las enfermedades transmitidas por el Aedes aegypti. Finalmente, tanto la estratificación de riesgo como los indicadores obtenidos en cada localidad son escalados a la unidad notificante, encarcada de la ratificación, análisis y toma de desiciones jurisdiccionales. Se realizó una prueba piloto en 50 distritos de la jurisdicción de Lima Metropolitana. Se encontró que once distritos fueron estratificados en escenario II, y cuatro distritos en escenario III(AU)

The current methodological proposal is part of the first two objectives of the National Plan to Fight Dengue, which are to promote, coordinate and facilitate the implementation of effective and timely strategies for the control and treatment of dengue, with special emphasis on geographical areas. of the country that present a high potential epidemic risk of this disease and manage technical cooperation for the control and management of dengue. In the initial phase, the activities and variables sensitive to monitoring were determined, based on the objectives and indicators identified within the technical and regulatory framework in force in Peru and the health framework established by the WHO, corresponding to surveillance, prevention, and control; but also, to the promotion of healthy practices. Then, the non-functional requirements of the monitoring platform were defined for the development of an application accessible through a browser on any device with Internet access, to be hosted on the Cloud Computing platform. Subsequently, the flowchart was made to monitor the classification of epidemiological scenarios and entomological risk stratification of diseases transmitted by Aedes aegypti. Finally, both the risk stratification and the indicators obtained in each locality are escalated to the notifying unit, in charge of the ratification, analysis and making of jurisdictional decisions. A pilot test was carried out in 50 districts of the jurisdiction of Metropolitan Lima. It was found that eleven districts were stratified in stage II, and four districts in stage III(AU)

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ABSTRACT Objective: To describe the main functions of the "Systematic Review Support" web-based system for removing duplicate articles and aiding eligibility analysis during the process of conducting systematic review studies. Methods: The system was developed based on the incremental build model using the Agile methodology. The software is proprietary source code and was published on a proprietary platform. The architecture of the production environment allows the infrastructure used to increase or decrease according to demand. The system functions are presented with insertion of screenshots of the interfaces of the version for personal computers during the simulation of a systematic review. Results: After importing the files containing the abstracts retrieved from the Pubmed, Embase, and Web of Science databases, the system identifies and removes duplicates for later reading and analysis of title and abstract, a stage which can be performed by one or more reviewers independently. After unblinding of reviewers, the decisions on the eligibility of the studies are compared automatically to help the researchers reach a consensus on any disagreements. Results can be filtered and a PDF produced containing the eligible studies. Conclusion: Version 1.0 of the system is available on the web (sysrev.azurewebsites.net) to assist researchers in the initial stages of systematic reviews.

RESUMO Objetivo: Descrever as principais funcionalidades do sistema "Apoio à Revisão Sistemática" na identificação e exclusão de artigos duplicados e no auxílio na análise de elegibilidade durante a condução de estudo de revisão sistemática. Métodos: O sistema foi desenvolvido com base em um modelo de processo incremental, utilizando-se metodologia Ágil. É de código fechado e foi publicado em plataforma proprietária. O ambiente de produção onde o sistema foi implantado possui arquitetura que permite que a infraestrutura utilizada aumente ou diminua conforme a demanda. As funcionalidades foram apresentadas com inserção de imagens das interfaces da versão para computadores, simulando uma revisão sistemática. Resultados: Após a importação dos resumos recuperados nas bases de dados PubMed, Embase e Web of Science, o sistema permite a identificação e eliminação de duplicatas para posterior leitura e análise de título e resumo, etapa que pode ser realizada por mais de um revisor de maneira independente. Após a quebra do cegamento entre os revisores, as respostas sobre a elegibilidade dos estudos podem ser comparadas automaticamente para facilitar a resolução de divergências pelos pesquisadores. É possível filtrar os resultados e gerar um arquivo PDF com os estudos elegíveis. Conclusão: A versão 1.0 do sistema "Apoio à Revisão Sistemática" encontra-se disponível na web (sysrev.azurewebsites.net) para auxiliar pesquisadores nas etapas iniciais de um estudo de revisão sistemática.

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The high demand for data processing in web applications has grown in recent years due to the increased computing infrastructure supply as a service in a cloud computing ecosystem. This ecosystem offers benefits such as broad network access, elasticity, and resource sharing, among others. However, properly exploiting these benefits requires optimized provisioning of computational resources in the target infrastructure. Several studies in the literature improve the quality of this management, which involves enhancing the scalability of the infrastructure, either through cost management policies or strategies aimed at resource scaling. However, few studies adequately explore performance evaluation mechanisms. In this context, we present the MoHRiPA-Management of Hybrid Resources in Private cloud Architecture. MoHRiPA has a modular design encompassing scheduling algorithms, virtualization tools, and monitoring tools. The proposed architecture solution allows assessing the overall system's performance by using complete factorial planning to identify the general behavior of architecture under high demand of requests. It also evaluates workload behavior, the number of virtualized resources, and provides an elastic resource manager. A composite metric is also proposed and adopted as a criterion for resource scaling. This work presents a performance evaluation by using formal techniques, which analyses the scheduling algorithms of architecture and the experiment bottlenecks analysis, average response time, and latency. In summary, the proposed MoHRiPA mapping resources algorithm (HashRefresh) showed significant improvement results than the analyzed competitor, decreasing about 7% percent in the uniform average compared to ListSheduling (LS).

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The data produced by sensors of IoT devices are becoming keystones for organizations to conduct critical decision-making processes. However, delivering information to these processes in real-time represents two challenges for the organizations: the first one is achieving a constant dataflow from IoT to the cloud and the second one is enabling decision-making processes to retrieve data from dataflows in real-time. This paper presents a cloud-based Web of Things method for creating digital twins of IoT devices (named sentinels).The novelty of the proposed approach is that sentinels create an abstract window for decision-making processes to: (a) find data (e.g., properties, events, and data from sensors of IoT devices) or (b) invoke functions (e.g., actions and tasks) from physical devices (PD), as well as from virtual devices (VD). In this approach, the applications and services of decision-making processes deal with sentinels instead of managing complex details associated with the PDs, VDs, and cloud computing infrastructures. A prototype based on the proposed method was implemented to conduct a case study based on a blockchain system for verifying contract violation in sensors used in product transportation logistics. The evaluation showed the effectiveness of sentinels enabling organizations to attain data from IoT sensors and the dataflows used by decision-making processes to convert these data into useful information.

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The management practicality and economy offered by the various technological solutions based on cloud computing have attracted many organizations, which have chosen to migrate services to the cloud, despite the numerous challenges arising from this migration. Cloud storage services are emerging as a relevant solution to meet the legal requirements of maintaining custody of electronic documents for long periods. However, the possibility of losses and the consequent financial damage require the permanent monitoring of this information. In a previous work named "Monitoring File Integrity Using Blockchain and Smart Contracts", the authors proposed an architecture based on blockchain, smart contract, and computational trust technologies that allows the periodic monitoring of the integrity of files stored in the cloud. However, the experiments carried out in the initial studies that validated the architecture included only small- and medium-sized files. As such, this paper presents a validation of the architecture to determine its effectiveness and efficiency when storing large files for long periods. The article provides an improved and detailed description of the proposed processes, followed by a security analysis of the architecture. The results of both the validation experiments and the implemented defense mechanism analysis confirm the security and the efficiency of the architecture in identifying corrupted files, regardless of file size and storage time.

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ABSTRACT Motion capture is a common problem in sports. It is mainly used to measure and capture running distance in football matches. Use of cloud computing motion capture algorithm in football running distance test, for lack of cloud computing algorithm using motion capture in the application. Data are obtained by experiment to verify the effectiveness and feasibility of the improved cloud computing motion capture algorithm in running distance in football. The final conclusion is that, compared with the original cloud computing motion capture algorithm, the optimized cloud computing motion capture algorithm can significantly reduce the number of cycles in the test of football running distance.

RESUMO A captura de movimento é um problema comum nos esportes. É usado principalmente para medir e capturar a distância de corrida em jogos de futebol. Este estudo aborda o uso do algoritmo de captura de movimento por computação em nuvem no teste de distância de corrida de futebol, por falta do algoritmo de computação em nuvem usando a captura de movimento na aplicação. Os dados são obtidos por experimento para verificar a eficácia e viabilidade do algoritmo melhorado de captura de movimento por computação em nuvem no teste de distância de corrida no futebol. A conclusão final é que, em comparação com o algoritmo original de captura de movimento por computação em nuvem, o algoritmo otimizado de captura de movimento por computação em nuvem pode reduzir significativamente o número de ciclos no teste de distância de corrida no futebol.

RESUMEN La captura de movimiento es un problema común en los deportes. Es usado principalmente para medir y capturar la distancia de corrida en juegos de fútbol. Este estudio aborda el uso del algoritmo de captura de movimiento por computación en nube en el test de distancia de corrida de fútbol, por falta del algoritmo de computación en nube usando la captura de movimiento en la aplicación. Los datos son obtenidos por experimento para verificar la eficacia y viabilidad del algoritmo mejorado de captura de movimiento por computación en nube en el test de distancia de corrida en el fútbol. La conclusión final es que, en comparación con el algoritmo original de captura de movimiento por computación en nube, el algoritmo optimizado de captura de movimiento por computación en nube puede reducir significativamente el número de ciclos en el test de distancia de corrida en el fútbol.

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ABSTRACT For athletes under training, it is more efficient to use the Internet of Things (IoT) and cloud computing methods to collect and process biochemical indicators, and this study is about research based on the IoT and cloud computing technology for athletes under training. The problems are put forward in this study. The requirements of related algorithm design and the communication model properties are comprehensively analyzed. Scheduling the link and allocating the transmit power of the nodes are comprehensively considered, with design and analysis of wireless sensor network scheduling algorithm. The factors influencing the scheduling efficiency of the algorithm are analyzed, considering the node density and the influence of different power allocation schemes on the scheduling result. This study shows that the algorithm of this thesis can collect the biochemical index data of athletes during training period. As the number of nodes increases, the running results will gradually move towards the optimal value. This research study is of important theoretical significance for the application of IoT and cloud computing technology and the improvement of athlete training effect.

RESUMO Para os indicadores bioquímicos dos atletas durante o treino, é mais eficiente usar a internet das coisas e métodos de computação em nuvem para coletar e processar indicadores bioquímicos durante o treino de atletas. Este estudo se baseia na tecnologia da internet das coisas IoT e na computação em nuvem voltada para atletas durante o período de treino. Os problemas são apresentados neste documento. Os requisitos de concepção de algoritmos relacionados e propriedades do modelo de comunicação são amplamente analisados. A programação do link e a alocação da potência de transmissão dos nodos são considerados de forma abrangente, com projeto e análise do algoritmo de programação da rede de sensores sem fio. Os fatores que influenciam a eficiência de programação do algoritmo são analisados, considerando a densidade do nodo e a influência de diferentes sistemas de alocação de energia no resultado da programação. A pesquisa Mostra que o algoritmo desta tese pode coletar os dados do índice bioquímico dos atletas durante o período de treino. À medida que o número de nodos aumenta, os resultados de execução tenderão gradualmente para o valor ideal. Esta pesquisa tem um significado teórico importante para a aplicação da tecnologia da internet das coisas e computação em nuvem e para a melhoria do efeito dos treinos realizados por atletas.

RESUMEN Para los indicadores bioquímicos de los atletas durante el entrenamiento, es más eficiente usar la internet de las cosas y métodos de computación en nube para recolectar y procesar indicadores bioquímicos durante el entrenamiento de atletas. Este estudio se basa en la tecnología de la internet de las cosas IoT y en la computación en nube dedicada a atletas durante el período de entrenamiento. Los problemas son presentados en este documento. Los requisitos de concepción de algoritmos relacionados y propriedades del modelo de comunicación son ampliamente analizados. La programación del link y la destinación de la potencia de transmisión de los nodos son considerados de forma abarcadora, con proyecto y análisis del algoritmo de programación de la red de sensores inalámbrica. Los fatores que influencian la eficiencia de programación del algoritmo son analizados, considerando la densidad del nodo y la influencia de diferentes sistemas de destinación de energía en el resultado de la programación. La investigación muestra que el algoritmo de esta tesis puede recolectar los datos del índice bioquímico de los atletas durante el período de entrenamiento. A medida que el número de nodos aumenta, los resultados de ejecución tenderán gradualmente hacia el valor ideal. Esta investigación tiene un significado teórico importante para la aplicación de la tecnología de la internet de las cosas y computación en nube y para la mejora del efecto de los entrenamientos realizados por atletas.

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This work illustrates the analysis of Film Bulk Acoustic Resonators (FBAR) using 3D Finite Element (FEM) simulations with the software OnScale in order to predict and improve resonator performance and quality before manufacturing. This kind of analysis minimizes manufacturing cycles by reducing design time with 3D simulations running on High-Performance Computing (HPC) cloud services. It also enables the identification of manufacturing effects on device performance. The simulation results are compared and validated with a manufactured FBAR device, previously reported, to further highlight the usefulness and advantages of the 3D simulations-based design process. In the 3D simulation results, some analysis challenges, like boundary condition definitions, mesh tuning, loss source tracing, and device quality estimations, were studied. Hence, it is possible to highlight that modern FEM solvers, like OnScale enable unprecedented FBAR analysis and design optimization.

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ABSTRACT The impact of traditional public health emergencies on the comprehensive education of medical students in colleges and universities is mainly reflected in the test of comprehensive literacy. Based on this, this paper studies the construction of a public health emergency impact analysis platform from a medical perspective and cloud computing. From the platform's database construction, event collection methods, impact evaluation rules and other aspects to achieve accurate analysis of the impact of emergencies, using the cloud computing method for comprehensive analysis and evaluation, the algorithm can analyze and intelligently classify information data on the Internet in the process of multiple input, and respond to potential public health emergencies according to cloud computing technology, in order to analyze the impact on the comprehensive quality of medical students. The experimental results show that the public health emergency analysis platform has the advantages of high feasibility and high data utilization, and can effectively improve the impact of public health emergencies on the comprehensive literacy of medical students.

RESUMO O impacto das tradicionais emergências de saúde pública sobre a formação integral de estudantes de medicina em faculdades e universidades reflete-se principalmente no teste de formação integral. Com base nisso, este documento estuda a construção da plataforma de análise de impacto de emergência de saúde pública sob a perspectiva médica e computação em nuvem. A partir da construção da base de dados da plataforma, foram desenvolvidos métodos de coleta de eventos, regras de avaliação de impacto e outros aspectos para obter uma análise precisa do impacto das emergências, usando o método de computação em nuvem para análise e avaliação. O algoritmo pode realizar a análise e classificação inteligente de dados de informação na Internet no processo de introdução múltipla, e responder a possíveis emergências de saúde pública de acordo com a tecnologia de computação em nuvem a fim de analisar o impacto sobre a qualificação dos estudantes de medicina. Os resultados experimentais mostram que a plataforma de análise de emergências de saúde pública tem as vantagens de alta viabilidade e alta utilização de dados, pode melhorar efetivamente o impacto das emergências de saúde pública na formação integral dos estudantes de medicina.

RESUMEN El impacto de las emergencias de salud pública tradicionales en la educación integral de los estudiantes de medicina en los colegios y universidades se refleja principalmente en la prueba de comprensión de textos. Con base en esto, este trabajo estudia la construcción de una plataforma de análisis de impacto de emergencias en salud pública desde una perspectiva médica y de computación en la nube. A partir de la construcción de la base de datos de la plataforma, los métodos de recolección de eventos, las reglas de evaluación de impacto y otros aspectos para lograr un análisis preciso del impacto de las emergencias, utilizando el método de computación en la nube para un análisis y evaluación integral, el algoritmo puede analizar y clasificar de manera inteligente los datos de información en Internet en el proceso de entrada múltiple. También puede responder a potenciales emergencias de salud pública de acuerdo con la tecnología de computación en la nube, con el fin de analizar el impacto en la calidad integral de los estudiantes de medicina. Los resultados experimentales muestran que la plataforma de análisis de emergencias de salud pública tiene las ventajas de alta viabilidad y alta utilización de datos, y puede mejorar de manera efectiva el impacto de las emergencias de salud pública en la comprensión de textos de los estudiantes de medicina.

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This work explores the combination of free cloud computing, free open-source software, and deep learning methods to analyze a real, large-scale problem: the automatic country-wide identification and classification of surface mines and mining tailings dams in Brazil. Locations of officially registered mines and dams were obtained from the Brazilian government open data resource. Multispectral Sentinel-2 satellite imagery, obtained and processed at the Google Earth Engine platform, was used to train and test deep neural networks using the TensorFlow 2 application programming interface (API) and Google Colaboratory (Colab) platform. Fully convolutional neural networks were used in an innovative way to search for unregistered ore mines and tailing dams in large areas of the Brazilian territory. The efficacy of the approach is demonstrated by the discovery of 263 mines that do not have an official mining concession. This exploratory work highlights the potential of a set of new technologies, freely available, for the construction of low cost data science tools that have high social impact. At the same time, it discusses and seeks to suggest practical solutions for the complex and serious problem of illegal mining and the proliferation of tailings dams, which pose high risks to the population and the environment, especially in developing countries.

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Cloud Manufacturing (CMFg) is a novel production paradigm that benefits from Cloud Computing in order to develop manufacturing systems linked by the cloud. These systems, based on virtual platforms, allow direct linkage between customers and suppliers of manufacturing services, regardless of geographical distance. In this way, CMfg can expand both markets for producers, and suppliers for customers. However, these linkages imply a new challenge for production planning and decision-making process, especially in Scheduling. In this paper, a systematic literature review of articles addressing scheduling in Cloud Manufacturing environments is carried out. The review takes as its starting point a seminal study published in 2019, in which all problem features are described in detail. We pay special attention to the optimization methods and problem-solving strategies that have been suggested in CMfg scheduling. From the review carried out, we can assert that CMfg is a topic of growing interest within the scientific community. We also conclude that the methods based on bio-inspired metaheuristics are by far the most widely used (they represent more than 50% of the articles found). On the other hand, we suggest some lines for future research to further consolidate this field. In particular, we want to highlight the multi-objective approach, since due to the nature of the problem and the production paradigm, the optimization objectives involved are generally in conflict. In addition, decentralized approaches such as those based on game theory are promising lines for future research.

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Fog computing is a distributed infrastructure where specific resources are managed at the network border using cloud computing principles and technologies. In contrast to traditional cloud computing, fog computing supports latency-sensitive applications with less energy consumption and a reduced amount of data traffic. A fog device is placed at the network border, allowing data collection and processing to be physically close to their end-users. This characteristic is essential for applications that can benefit from improved latency and response time. In particular, in the e-Health field, many solutions rely on real-time data to monitor environments, patients, and/or medical staff, aiming at improving processes and safety. Therefore, fog computing can play an important role in such environments, providing a low latency infrastructure. The main goal of the current research is to present fog computing strategies focused on electronic-Health (e-Health) applications. To the best of our knowledge, this article is the first to propose a review in the scope of applications and challenges of e-Health fog computing. We introduce some of the available e-Health solutions in the literature that focus on latency, security, privacy, energy efficiency, and resource management techniques. Additionally, we discuss communication protocols and technologies, detailing both in an architectural overview from the edge devices up to the cloud. Differently from traditional cloud computing, the fog concept demonstrates better performance in terms of time-sensitive requirements and network data traffic. Finally, based on the evaluation of the current technologies for e-Health, open research issues and challenges are identified, and further research directions are proposed.

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The industrial applications in the cloud do not meet the requirements of low latency and reliability since variables must be continuously monitored. For this reason, industrial internet of things (IIoT) is a challenge for the current infrastructure because it generates a large amount of data making cloud computing reach the edge and become fog computing (FC). FC can be considered as a new component of Industry 4.0, which aims to solve the problem of big data, reduce energy consumption in industrial sensor networks, improve the security, processing and storage real-time data. It is a promising growing paradigm that offers new opportunities and challenges, beside the ones inherited from cloud computing, which requires a new heterogeneous architecture to improve the network capacity for delivering edge services, that is, providing computing resources closer to the end user. The purpose of this research is to show a systematic review of the most recent studies about the architecture, security, latency, and energy consumption that FC presents at industrial level and thus provide an overview of the current characteristics and challenges of this new technology.

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Scientific workflows can be understood as arrangements of managed activities executed by different processing entities. It is a regular Bioinformatics approach applying workflows to solve problems in Molecular Biology, notably those related to sequence analyses. Due to the nature of the raw data and the in silico environment of Molecular Biology experiments, apart from the research subject, 2 practical and closely related problems have been studied: reproducibility and computational environment. When aiming to enhance the reproducibility of Bioinformatics experiments, various aspects should be considered. The reproducibility requirements comprise the data provenance, which enables the acquisition of knowledge about the trajectory of data over a defined workflow, the settings of the programs, and the entire computational environment. Cloud computing is a booming alternative that can provide this computational environment, hiding technical details, and delivering a more affordable, accessible, and configurable on-demand environment for researchers. Considering this specific scenario, we proposed a solution to improve the reproducibility of Bioinformatics workflows in a cloud computing environment using both Infrastructure as a Service (IaaS) and Not only SQL (NoSQL) database systems. To meet the goal, we have built 3 typical Bioinformatics workflows and ran them on 1 private and 2 public clouds, using different types of NoSQL database systems to persist the provenance data according to the Provenance Data Model (PROV-DM). We present here the results and a guide for the deployment of a cloud environment for Bioinformatics exploring the characteristics of various NoSQL database systems to persist provenance data.

RESUMO

In the fog computing paradigm, fog nodes are placed on the network edge to meet end-user demands with low latency, providing the possibility of new applications. Although the role of the cloud remains unchanged, a new network infrastructure for fog nodes must be created. The design of such an infrastructure must consider user mobility, which causes variations in workload demand over time in different regions. Properly deciding on the location of fog nodes is important to reduce the costs associated with their deployment and maintenance. To meet these demands, this paper discusses the problem of locating fog nodes and proposes a solution which considers time-varying demands, with two classes of workload in terms of latency. The solution was modeled as a mixed-integer linear programming formulation with multiple criteria. An evaluation with real data showed that an improvement in end-user service can be obtained in conjunction with the minimization of the costs by deploying fewer servers in the infrastructure. Furthermore, results show that costs can be further reduced if a limited blocking of requests is tolerated.