Aplicação de regras associação para mineração de dados em uma rede mesh de transdutores inteligentes baseada nos padrões ZigBee e IEEE1451.5
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Data
2020-09-08
Autores
Ribeiro, Juliano Mendes
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Universidade Estadual Paulista (Unesp)
Resumo
Neste trabalho desenvolveu-se uma rede mesh de transdutores inteligentes baseada nos padrões ZigBee e IEEE 1451.5. Após a implantação da rede, realizou-se um estudo sobre a aplicação da mineração de regras de associação nos dados da rede, para descobrir padrões entre as variáveis climáticas em um ambiente urbano. A mineração de regras de associação é um aprendizado de máquina baseado em regras, para descobrir relações entre as variáveis em grandes bases de dados. Na rede desenvolvida existem dois tipos de nós, de acordo com as diretrizes do padrão IEEE 1451.5, denominados de: Network Capable Application Processor (NCAP) e Wireless Transducer Interface Module (WTIM). Neste trabalho, o NCAP é executado de forma autônoma, realizando a leitura dos sensores conectados aos WTIMs de acordo com períodos de leituras pré-determinados. Após a aquisição dos dados dos sensores realiza-se o processamento e armazenamento no NCAP em memória flash local. Após um período de monitoramento, os dados da rede desenvolvida foram submetidos à mineração de regras de associação por meio do algoritmo Apriori. Constatou-se que esta técnica é capaz de descobrir padrões em dados climáticos com alto grau de complexidade. Logo, confirma-se a viabilidade da aplicação desta técnica em dados adquiridos em uma rede mesh de transdutores inteligentes.
In this work, a mesh network of intelligent transducers based on the ZigBee and IEEE 1451.5 standards was developed. After the implantation of the network, a study was carried out on the application of mining association rules in the network data, to discover patterns between climatic variables in an urban environment. Association rules mining is a rule-based machine learning to discover relationships between variables in large databases. In the developed network there are two types of nodes, according to the guidelines of the IEEE 1451.5 standard, called: Network Capable Application Processor (NCAP) and Wireless Transducer Interface Module (WTIM). In this work, the NCAP is executed autonomously, reading the sensors connected to the WTIMs according to predetermined reading periods. After the acquisition of sensor data, NCAP is processed and stored in local ash memory. After a period of monitoring, the data from the developed network were subjected to mining association rules using the Apriori algorithm. It was found that this technique is able to discover patterns in climatic data with a high degree of complexity. Therefore, the feasibility of applying this technique to data acquired in a mesh network of intelligent transducers is con rmed.
In this work, a mesh network of intelligent transducers based on the ZigBee and IEEE 1451.5 standards was developed. After the implantation of the network, a study was carried out on the application of mining association rules in the network data, to discover patterns between climatic variables in an urban environment. Association rules mining is a rule-based machine learning to discover relationships between variables in large databases. In the developed network there are two types of nodes, according to the guidelines of the IEEE 1451.5 standard, called: Network Capable Application Processor (NCAP) and Wireless Transducer Interface Module (WTIM). In this work, the NCAP is executed autonomously, reading the sensors connected to the WTIMs according to predetermined reading periods. After the acquisition of sensor data, NCAP is processed and stored in local ash memory. After a period of monitoring, the data from the developed network were subjected to mining association rules using the Apriori algorithm. It was found that this technique is able to discover patterns in climatic data with a high degree of complexity. Therefore, the feasibility of applying this technique to data acquired in a mesh network of intelligent transducers is con rmed.
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Palavras-chave
Redes mesh, Mineração de dados, Regras de associação, ZigBee, IEEE 1451.5, Mesh networks, Data mining, Association rules