Big Data em cidades inteligentes: um mapeamento sistemático

O conceito de Cidades Inteligentes ganhou maior atenção nos círculos acadêmicos, industriais e governamentais. À medida que a cidade se desenvolve ao longo do tempo, componentes e subsistemas como redes inteligentes, gerenciamento inteligente de água, tráfego inteligente e sistemas de transporte, sistemas de gerenciamento de resíduos inteligentes, sistemas de segurança inteligentes ou governança eletrônica são adicionados. Esses componentes ingerem e geram uma grande quantidade de dados estruturados, semiestruturados ou não estruturados que podem ser processados usando uma variedade de algoritmos em lotes, microlotes ou em tempo real, visando a melhoria de qualidade de vida dos cidadãos. Esta pesquisa secundária tem como objetivo facilitar a identificação de lacunas neste campo, bem como alinhar o trabalho dos pesquisadores com outros para desenvolver temas de pesquisa mais fortes. Neste estudo, é utilizada a metodologia de pesquisa formal de mapeamento sistemático para fornecer uma revisão abrangente das tecnologias de Big Data na implantação de cidades inteligentes

Sehaa: A big data analytics tool for healthcare symptoms and diseases detection using Twitter, Apache Spark, and Machine Learning

Smartness, which underpins smart cities and societies, is defined by our ability to engage with our environments, analyze them, and make decisions, all in a timely manner. Healthcare is the prime candidate needing the transformative capability of this smartness. Social media could enable a ubiquitous and continuous engagement between healthcare stakeholders, leading to better public health. Current works are limited in their scope, functionality, and scalability. This paper proposes Sehaa, a big data analytics tool for healthcare in the Kingdom of Saudi Arabia (KSA) using Twitter data in Arabic. Sehaa uses Naive Bayes, Logistic Regression, and multiple feature extraction methods to detect various diseases in the KSA. Sehaa found that the top five diseases in Saudi Arabia in terms of the actual aicted cases are dermal diseases, heart diseases, hypertension, cancer, and diabetes. Riyadh and Jeddah need to do more in creating awareness about the top diseases. Taif is the healthiest city in the KSA in terms of the detected diseases and awareness activities. Sehaa is developed over Apache Spark allowing true scalability. The dataset used comprises 18.9 million tweets collected from November 2018 to September 2019. The results are evaluated using well-known numerical criteria (Accuracy and F1-Score) and are validated against externally available statistics

A Bibliometric Diagnosis and Analysis about Smart Cities

[EN] This article aims to present a bibliometric analysis of Smart Cities. The study analyzes the most important journals during the period between 1991 and 2019. It provides helpful insights into the document types, the distribution of countries/territories, the distribution of institutions, the authors' geographical distribution, the most active authors and their research interests or fields, the relationships between principal authors and more relevant publications, and the most cited articles. This paper also provides important information about the core and historical references and the most cited papers. The analysis used the keywords and thematic noun-phrases in the titles and abstracts of the sample papers to explore the hot research topics in the top journals (e.g., 'Smart Cities', 'Intelligent Cities', 'Sustainable Cities', 'e-Government', 'Digital Transformation', 'Knowledge-Based City', etc.). The main objective is to have a quantitative description of the published literature about Smart Cities; this description will be the basis for the development of a methodology for the diagnosis of the maturity of a Smart City. The results presented here help to define the scientific concept of Smart Cities and to measure the importance that the term has gained through the years. The study has allowed us to know the main indicators of the published literature in depth, from the date of publication of the first articles and the evolution of these indicators to the present day. From the main indicators in the literature, some were selected to be applied: The most influential journals on Smart Cities according to the general citation structure in Smart Cities, Global Impact Factor of Smart Cities, number of publications, publications on Smart Cities around the world, and their correlation.Pérez, LM.; Oltra Badenes, RF.; Oltra Gutiérrez, JV.; Gil Gómez, H. (2020). 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Mechanisms for service-oriented resource allocation in IoT

Albeit several IoT applications have been recently deployed in several fields, including environment and industry monitoring, Smart Home, Smart Hospital and Smart Agriculture, current deployments are mostly host-oriented, which is undoubtedly limiting the attained benefits brought up by IoT. Indeed, future IoT applications shall benefit from service-oriented communications, where the communication establishment between end-points is not dependent on prior knowledge of the host devices in charge of providing the service execution. Rather, an end-user service execution request is mapped into the most suitable resources able to provide the requested service. Furthermore, this model is a key enabler for the design of future services in Smart Cities, e-Health, Intelligent Transportation Systems, among other smart scenarios. Recognized the benefits of this model in future applications, considerable research effort must be devoted for addressing several challenges yet unsolved, such as the ones brought up by the high dynamicity and heterogeneity inherent to these scenarios. In fact, service-oriented communication requires an updated view of available resources, mapping service requests into the most suitable resources taking several constraints and requirements into account, resilience provisioning, QoS-aware service allocation, just to name a few. This thesis aims at proposing and evaluating mechanisms for efficient resource allocation in service-oriented IoT scenarios through the employment of two distinct baseline technologies. In the first approach, the so-called Path Computation Element (PCE), designed to decouple the host-oriented routing function from GMPLS switches in a centralized element, is extended to the service-oriented PCE (S-PCE) architecture, where a service identifier (SID) is used to identify the service required by an end-user. In this approach, the service request is mapped to one or a set of resources by a 2-steps mapping scheme that enables both selection of suitable resources according to request and resources characteristics, and avoidance of service disruption due to possible changes on resources¿ location. In the meantime, the inception of fog computing, as an extension of the cloud computing concept, leveraging idle computing resources at the edge of the network through their organization as highly virtualized micro data centers (MDC) enabled the reduction on the network latency observed by services launched at edge devices, further reducing the traffic at the core network and the energy consumption by network and cloud data center equipment, besides other benefits. Envisioning the benefits of the distributed and coordinated employment of both fog and cloud resources, the Fog-to-Cloud (F2C) architecture has been recently proposed, further empowering the distributed allocation of services into the most suitable resources, be it in cloud, fog or both. Since future IoT applications shall present strict demands that may be satisfied through a combined fog-cloud solution, aligned to the F2C architecture, the second approach for the service-oriented resource allocation, considered in this thesis, aims at providing QoS-aware resource allocation through the deployment of a hierarchical F2C topology, where resource are logically distributed into layers providing distinct characteristics in terms of network latency, disruption probability, IT power, etc. Therefore, distinct strategies for service distribution in F2C architectures, taking into consideration features such as service transmission delay, energy consumption and network load. Concerning the need for failure recovery mechanisms, distinct demands of heterogeneous services are considered in order to assess distinct strategies for allocation of protection resources in the F2C hierarchy. In addition, the impact of the layered control topology on the efficient allocation of resources in F2C is further evaluated. Finally, avenues for future work are presented.Aunque son ya varias las aplicaciones que se han desarrollado en el área de IoT, especialmente en el campo ambiental, Smart Home o Smart Health, las implementaciones actuales son en su mayoría ¿host-oriented¿, lo que sin duda limita sus potenciales beneficios. Una posible estrategia para reducir esos efectos negativos se centra en que las futuras aplicaciones se beneficien de las comunicaciones orientadas a servicios, ¿service-oriented¿, donde el establecimiento de comunicación entre puntos finales no depende del conocimiento previo de los hosts a cargo de proporcionar la ejecución del servicio. En este escenario, una solicitud de ejecución de servicio se asigna a los recursos más adecuados capaces de proporcionar el servicio solicitado. Este modelo se considera clave para el despliegue de futuros servicios en Smart Cities, e-Health, Intelligent Transportation Systems, etc. Reconocidos los beneficios de este modelo en las aplicaciones futuras, un substancial esfuerzo de investigación es necesario para abordar varios desafíos aún no resueltos, como los surgidos por la alta dinámica y heterogeneidad inherente a estos escenarios. De hecho, la comunicación service-oriented requiere una vista actualizada de los recursos disponibles, así como la asignación de solicitudes de servicio en los recursos más adecuados teniendo en cuenta varias restricciones y requisitos. Esta tesis tiene como objetivo proponer y evaluar mecanismos para la asignación eficiente de recursos en escenarios IoT orientados a servicios a través del empleo de dos tecnologías básicas distintas. En el primer enfoque, el llamado Path Computation Element (PCE), diseñado para desacoplar la función de enrutamiento de los conmutadores GMPLS hacia un elemento centralizado, se extiende generando la arquitectura service-oriented PCE (S-PCE). En S-PCE se utiliza un identificador de servicio (SID) para identificar el servicio requerido por un usuario final, y la solicitud se asigna, bien a uno o bien a un conjunto de recursos, mediante un esquema de asignación de 2 pasos que permite la selección de los recursos adecuados, evitando la interrupción del servicio debido a posibles cambios en la ubicación de los recursos. Mientras tanto, el inicio de Fog computing, como una extensión de Cloud computing, basado conceptualmente en aprovechar la infraestructura y los recursos inactivos en el extremo de la red a través de su organización como micro data centers (MDC), ha supuesto la reducción de la latencia de la red para los servicios lanzados por dispositivos localizados en el extremo de la red, reduciendo el tráfico en el centro de la red (backbone) así como el consumo de energía, además de otros beneficios. Asumiendo las ventajas de la utilización distribuida y coordinada de los recursos fog y cloud, la arquitectura Fog-to-Cloud (F2C) ha sido recientemente propuesta, destinada a potenciar la asignación distribuida de servicios en los recursos más adecuados, sea en cloud, fog o ambos. Dado que las futuras aplicaciones IoT deben presentar demandas que podrían ser satisfechas a través de una solución alineada con la arquitectura F2C, el segundo enfoque para la asignación de recurso orientado a servicio, considerado en esta tesis, tiene como objetivo proporcionar una asignación de recursos mediante el despliegue de una topología F2C, donde los recursos se distribuyen lógicamente en capas que proporcionan características distintas en términos de latencia de red, probabilidad de interrupción, etc. Así, se proponen distintas estrategias para la distribución de servicios, teniendo en cuenta características tales como QoS y consumo de energía. Con respecto a la necesidad de mecanismos de recuperación de fallos, se evalúan distintas estrategias para la asignación de recursos de protección en la jerarquía F2C. Además, se evalúa el impacto de la topología de control en capas sobre la asignación eficiente de recursos en F2C. Finalmente, las sugerencias para trabajos futuros son presentadas

Internet of Things and data mining: from applications to techniques and systems

The Internet of Things (IoT) is the result of the convergence of sensing, computing, and networking technologies, allowing devices of varying sizes and computational capabilities (things) to intercommunicate. This communication can be achieved locally enabling what is known as edge and fog computing, or through the well‐established Internet infrastructure, exploiting the computational resources in the cloud. The IoT paradigm enables a new breed of applications in various areas including health care, energy management and smart cities. This paper starts off with reviewing these applications and their potential benefits. Challenges facing the realization of such applications are then discussed. The sheer amount of data stemmed from devices forming the IoT requires new data mining systems and techniques that are discussed and categorized later in this paper. Finally, the paper is concluded with future research directions

Green internet of things using UAVs in B5G networks: A review of applications and strategies

Recently, Unmanned Aerial Vehicles (UAVs) present a promising advanced technology that can enhance people life quality and smartness of cities dramatically and increase overall economic efficiency. UAVs have attained a significant interest in supporting many applications such as surveillance, agriculture, communication, transportation, pollution monitoring, disaster management, public safety, healthcare, and environmental preservation. Industry 4.0 applications are conceived of intelligent things that can automatically and collaboratively improve beyond 5G (B5G). Therefore, the Internet of Things (IoT) is required to ensure collaboration between the vast multitude of things efficiently anywhere in real-world applications that are monitored in real-time. However, many IoT devices consume a significant amount of energy when transmitting the collected data from surrounding environments. Due to a drone's capability to fly closer to IoT, UAV technology plays a vital role in greening IoT by transmitting collected data to achieve a sustainable, reliable, eco-friendly Industry 4.0. This survey presents an overview of the techniques and strategies proposed recently to achieve green IoT using UAVs infrastructure for a reliable and sustainable smart world. This survey is different from other attempts in terms of concept, focus, and discussion. Finally, various use cases, challenges, and opportunities regarding green IoT using UAVs are presented.This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 847577; and a research grant from Science Foundation Ireland (SFI) under Grant Number 16 / RC / 3918 (Ireland's European Structural and Investment Funds Programmes and the European Regional Development Fund 2014-2020)

The Internet of Things, fog and cloud continuum: Integration and challenges

The Internet of Things needs for computing power and storage are expected to remain on the rise in the next decade. Consequently, the amount of data generated by devices at the edge of the network will also grow. While cloud computing has been an established and effective way of acquiring computation and storage as a service to many applications, it may not be suitable to handle the myriad of data from IoT devices and fulfill largely heterogeneous application requirements. Fog computing has been developed to lie between IoT and the cloud, providing a hierarchy of computing power that can collect, aggregate, and process data from/to IoT devices. Combining fog and cloud may reduce data transfers and communication bottlenecks to the cloud and also contribute to reduced latencies, as fog computing resources exist closer to the edge. This paper examines this IoT-Fog-Cloud ecosystem and provides a literature review from different facets of it: how it can be organized, how management is being addressed, and how applications can benefit from it. Lastly, we present challenging issues yet to be addressed in IoT-Fog-Cloud infrastructures

A Framework for Controlling & Managing Traffic with Modern Technology

Thousands of people die on roads every day. Despite advances in technology, controlling vehicular traffic remains a challenge. Modern technologies can be very helpful in controlling and managing traffic. In particular, the Internet of Things (IoT) and Machine Learning (ML) have created a new level of automation of services and applications, which have given rise to smart cities, smart health, smart business, and smart transportation. In this thesis, we shall provide a framework for better and comprehensive control and management of road traffic with the help of modern technology. The proposed system has four layers, namely Application Service Layer, Cloud Computing Layer, Fog Computing Layer, and Sensing and Objects Layer. We shall provide details of these layers and associated levels. The First level of the proposed solution addresses the issue of traffic congestion at intersections, with a proposal of a dynamic smart traffic light with the help of Image processing algorithms. The second level will deal with the surveillance of selected streets to provide information about possible hazards. The third level deals with data mining and machine learning algorithms to add information to the knowledgebase to enable better planning. The fourth level proposes several applications to assist general services like school buses, ambulance, parking, places of interest, pollution, emergency cases, etc. Congestion often occurs during peak hours. One of the causes of increased volume of traffic in town centres and business districts is a lack of suitable carparks. In this thesis, we also provide a framework for smart parking which can be extremely helpful in preventing frantic searches for parking, which are a cause of congestion. Road users often use various services like toll tax, parking-fee services, etc. These often require users to furnish their data to the service provider, who could potentially breach their privacy. We shall discuss ways to preserve the location and ID of the users. We shall also provide a framework for assisting with emergencies like accidents. Several measures proposed in this thesis may also be used in other situations

Contributions to Edge Computing

Efforts related to Internet of Things (IoT), Cyber-Physical Systems (CPS), Machine to Machine (M2M) technologies, Industrial Internet, and Smart Cities aim to improve society through the coordination of distributed devices and analysis of resulting data. By the year 2020 there will be an estimated 50 billion network connected devices globally and 43 trillion gigabytes of electronic data. Current practices of moving data directly from end-devices to remote and potentially distant cloud computing services will not be sufficient to manage future device and data growth. Edge Computing is the migration of computational functionality to sources of data generation. The importance of edge computing increases with the size and complexity of devices and resulting data. In addition, the coordination of global edge-to-edge communications, shared resources, high-level application scheduling, monitoring, measurement, and Quality of Service (QoS) enforcement will be critical to address the rapid growth of connected devices and associated data. We present a new distributed agent-based framework designed to address the challenges of edge computing. This actor-model framework implementation is designed to manage large numbers of geographically distributed services, comprised from heterogeneous resources and communication protocols, in support of low-latency real-time streaming applications. As part of this framework, an application description language was developed and implemented. Using the application description language a number of high-order management modules were implemented including solutions for resource and workload comparison, performance observation, scheduling, and provisioning. A number of hypothetical and real-world use cases are described to support the framework implementation