Aprendizado de máquina para redução do tráfego de dados e da latência na névoa das coisas

• Autor(a) principal: Alencar, Brenno de Mello
• Data de Publicação: 2020
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UFBA
• Texto Completo: http://repositorio.ufba.br/ri/handle/ri/33634
• topic: Ciências Exatas e da Terra; Ciência da Computação; Sistemas de Computação; Internet das Coisas; Mineração de dados (Computação); Concept Drift; Wavelets (Matemática); Computação em Névoa; Névoa das Coisas; Redes neurais artificiais; Fluxo de dados (Computadores)

The Internet of Things (IoT) has produced infrastructures and applications that generate large amounts of data. These data are usually data streams, that have the characteristic of being continuous and infinite and also have the peculiarity of modifying their behavior over time. Due to the large capacity of storage, data processing, and provisioning of resources, this data is generally processed and analyzed in cloud computing environments. Although Cloud Computing provides the IoT infrastructure with adequate scalability and resource centric features, the distance between devices and the cloud can impose limitations to achieve low latency in data traffic. In order to maintain scalability, achieve low latency and reduce data traffic between the IoT devices and the Cloud, the Fog Computing was proposed. Although the Fog Computing paradigm establishes resource availability at the edge of the network, the technologies and techniques currently used for IoT data processing and analysis may not be sufficient to support the continuous and unlimited data stream that IoT platforms produce. In this way, this work presents an approach for processing and analyzing data stream from the Internet of Things in real time in Fog. The main advantage of using our approach is the possibility of reducing the amount of data transmitted on the network infrastructure, which allows, as a consequence, to perform an online data modeling, by detecting changes in data behavior, and a reduction of the Internet usage. In addition, the proposed platform does not require a constant Internet connection. Finally, we evaluate the proposal from the perspective of performance in a scenario of intelligent objects at the edge of the network.