SGeoL: uma plataforma para o desenvolvimento de aplicações para cidades inteligentes

• Ano de defesa: 2021
• Autor(a) principal: Silva, Jorge Pereira da
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Rio Grande do Norte; Brasil; UFRN; PROGRAMA DE PÓS-GRADUAÇÃO EM SISTEMAS E COMPUTAÇÃO
• Palavras-chave em Português: Computação; Cidades inteligentes; Middleware; Serviços web
• Link de acesso: https://repositorio.ufrn.br/handle/123456789/48505

In the last few decades, the number of people living in cities has grown exponentially. This scenario imposes several challenges to the management of the city, since the services offered to the population (transportation, security, health, electricity supply, etc.) need to be scaled up quickly to support an increasing number of inhabitants. The realization of the concept of smart cities emerged as a promising solution to face the various challenges resulting from urban growth. Smart city environments are characterized by the presence of a myriad of applications that aim to facilitate city management, contributing to the provision of more efficient services and, consequently, improving the quality of life of citizens. However, developing such applications is not a trivial task. In many cases, developers need to meet several complex requirements to be implemented. In addition, to allow contextualization and correlation of information produced in the city, they need to be enriched with geographical information that represents the urban space. In this sense, smart city development platforms play a fundamental role in achieving this environment. Such platforms provide high-level services that can be easily reused by developers to leverage application development. In this perspective, this work presents Smart Geo Layers (SGeoL), a scalable platform for developing applications for smart cities. In addition to allowing the integration of urban data with geographic information, SGeoL offers facilities for: i) management of context data, ii) integration of heterogeneous data, iii) semantic support; iv) data analysis and visualization; v) support for data security and privacy, etc. This work also presents experiences of real use of SGeoL in different scenarios, as well as results of computational experiments that evaluate its performance and scalability.