Big Data Analytics Embedded Smart City Architecture for Performance Enhancement through Real-Time Data Processing and Decision-Making

The concept of the smart city is widely favored, as it enhances the quality of life of urban citizens, involving multiple disciplines, that is, smart community, smart transportation, smart healthcare, smart parking, and many more. Continuous growth of the complex urban networks is significantly challenged by real-time data processing and intelligent decision-making capabilities. Therefore, in this paper, we propose a smart city framework based on Big Data analytics. The proposed framework operates on three levels: (1) data generation and acquisition level collecting heterogeneous data related to city operations, (2) data management and processing level filtering, analyzing, and storing data to make decisions and events autonomously, and (3) application level initiating execution of the events corresponding to the received decisions. In order to validate the proposed architecture, we analyze a few major types of dataset based on the proposed three-level architecture. Further, we tested authentic datasets on Hadoop ecosystem to determine the threshold and the analysis shows that the proposed architecture offers useful insights into the community development authorities to improve the existing smart city architecture

Análisis y aplicaciones de Internet de las cosas y ciudades inteligentes : Framework de trabajo

El desarrollo de las nuevas tecnologías como Internet de las Cosas (IoT) ha planteado nuevas formas de generar aplicaciones en pos de mejorar los servicios en las ciudades con un impacto directo en la calidad de vida de las personas y el medio ambiente. Este concepto está relacionado a ciudades inteligentes, seguridad y la gestión de las telecomunicaciones que interconectan múltiples dispositivos con una interacción humana mínima. Este trabajo se orienta a estudiar y proponer soluciones teniendo en consideración las arquitecturas necesarias, la cultura, los estudios de factibilidad técnico-económicos, el uso de las TICs, los aspectos climáticos, las normativas locales o nacionales de uso de telecomunicaciones y espectro, entre otros aspectos relevantes para proponer frameworks de IoT que puedan ser transferidos al medio local.Eje: Arquitectura, redes y sistemas operativos.Red de Universidades con Carreras en Informátic

Análisis y aplicaciones de internet de las cosas y ciudades inteligentes: escenario de testeo de seguridad

El rápido desarrollo de las nuevas tecnologías de Internet de las Cosas (IoT) como sus usos en diferentes campos de la industria, las ciudades, la salud, los hogares, entre otros, ha planteado nuevas formas de enfrentar amenazas de seguridad. IoT es una colección de dispositivos interconectados fortalecido con pequeños procesadores, placas o interfaces de red que se comunican con servicios web u otro tipo de interfaces a través de diferentes medios de telecomunicación. Naturalmente si una nueva tecnología es ampliamente adoptada por el público y hay una notoria falta de estándares para el campo, las amenazas de ciberseguridad crecen. En esta etapa del proyecto nos centramos en generar un escenario para realizar un relevamiento de los diferentes tipos de tráfico y crear un posterior datasets de análisis para poder identificar diferentes amenazas en sistemas IoT industriales o corporativos.Red de Universidades con Carreras en Informátic

Modelado de variedad de activos de dominio en sistemas big data

Un cambio importante con respecto a depósitos de datos tradicionales, es que en los Sistemas Big Data (SBDs) la naturaleza no estructurada de algunos datos puede provenir de diversas fuentes, entre ellas sensores, redes sociales, entorno y la misma empresa. La diversidad de esos datos puede analizarse abordando distintas características. Precisamente, la propiedad de los SBDs con respecto a diversidad de los datos se denomina Variedad. La variedad en SBDs ha sido relacionada con diversas propiedades como interoperabilidad, seguridad, reusabilidad, etc. En este contexto, y respondiendo a la pregunta de investigación: ¿Cómo puede modelarse la variedad de la información de dominio de manera de incorporar reusabilidad en el desarrollo de SBDs? , nuestro proyecto propone modelar variedad a modo de líneas de productos. A diferencia de otras propuestas, la nuestra toma como partida una estructura de actividades asociadas al desarrollo de SBDs, instanciada en artefactos software producidos durante esas actividades e incorpora el modelado de variedades de manera similar a líneas de productos.Red de Universidades con Carreras en Informátic

Reinforcement machine learning for predictive analytics in smart cities

The digitization of our lives cause a shift in the data production as well as in the required data management. Numerous nodes are capable of producing huge volumes of data in our everyday activities. Sensors, personal smart devices as well as the Internet of Things (IoT) paradigm lead to a vast infrastructure that covers all the aspects of activities in modern societies. In the most of the cases, the critical issue for public authorities (usually, local, like municipalities) is the efficient management of data towards the support of novel services. The reason is that analytics provided on top of the collected data could help in the delivery of new applications that will facilitate citizens’ lives. However, the provision of analytics demands intelligent techniques for the underlying data management. The most known technique is the separation of huge volumes of data into a number of parts and their parallel management to limit the required time for the delivery of analytics. Afterwards, analytics requests in the form of queries could be realized and derive the necessary knowledge for supporting intelligent applications. In this paper, we define the concept of a Query Controller ( QC ) that receives queries for analytics and assigns each of them to a processor placed in front of each data partition. We discuss an intelligent process for query assignments that adopts Machine Learning (ML). We adopt two learning schemes, i.e., Reinforcement Learning (RL) and clustering. We report on the comparison of the two schemes and elaborate on their combination. Our aim is to provide an efficient framework to support the decision making of the QC that should swiftly select the appropriate processor for each query. We provide mathematical formulations for the discussed problem and present simulation results. Through a comprehensive experimental evaluation, we reveal the advantages of the proposed models and describe the outcomes results while comparing them with a deterministic framework

Big Data Analytics Embedded Smart City Architecture for Performance Enhancement through Real-Time Data Processing and Decision-Making

The concept of the smart city is widely favored, as it enhances the quality of life of urban citizens, involving multiple disciplines, that is, smart community, smart transportation, smart healthcare, smart parking, and many more. Continuous growth of the complex urban networks is significantly challenged by real-time data processing and intelligent decision-making capabilities. Therefore, in this paper, we propose a smart city framework based on Big Data analytics. The proposed framework operates on three levels: (1) data generation and acquisition level collecting heterogeneous data related to city operations, (2) data management and processing level filtering, analyzing, and storing data to make decisions and events autonomously, and (3) application level initiating execution of the events corresponding to the received decisions. In order to validate the proposed architecture, we analyze a few major types of dataset based on the proposed three-level architecture. Further, we tested authentic datasets on Hadoop ecosystem to determine the threshold and the analysis shows that the proposed architecture offers useful insights into the community development authorities to improve the existing smart city architecture

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

Smart tourism – city tourism radar : a tourism monitoring tool at the city of Lisbon

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe increasing demand for Lisbon has led to an uncontrolled access to the city’s main attractions, which is reflected in the number of visitors that can be encountered at the city. Smart Tourism Destinations are gaining relevance in Smart Cities in everyday life, and technology is intricated more than ever in the cities and its citizens. Open Governance is a vital concept in any modern city and data is shared and available like never before. It is proposed a conceptual model to a city tourism dashboard and its materialization using Open Data from the city’s public portal, produced by the Lisbon City Council and other partners. It is also suggested a method to the conception of this tool and the main indicators that must be included based on the actual state of the art. It concludes with a proposal of future developments to perform on the smart tourism destinations area.O aumento da procura de Lisboa como destino turístico conduziu a um acesso descontrolado aos seus principais pontos turísticos, refletindo-se no elevado número de visitantes que se visitam a cidade. As Smart Tourism Destinations estão a ganhar cada vez mais importância no dia-a-dia das Smart Cities, e a tecnologia está cada vez mais intrínseca nas cidades e nos seus cidadãos. Open Governance é um conceito vital em qualquer cidade atual, já que existem dados e informação disponíveis hoje em dia como nunca existiram antes. Neste trabalho, é proposta uma framework conceptual para visualizar a informação adequada à tomada de decisão no turismo de uma cidade onde são apresentados os principais indicadores que devem ser incluídos na mesma, com base no estado de arte atual. É também sugerido um método de instanciação desta ferramenta, utilizando dados abertos do portal público de dados abertos produzidos pela Câmara Municipal de Lisboa e por outras entidades. Este trabalho é concluído com uma proposta de futuros desenvolvimentos a realizar na área de Smart Tourism Destinations