Nó sensor inteligente para detecção e classificação de derivados de petróleo em ambiente aquático

• Ano de defesa: 2024
• Autor(a) principal: SILVA, Yan Ferreira da
• Orientador(a): FONSECA NETO, João Viana da
• Banca de defesa: SOUZA, Francisco das Chagas de , CASAS, Vicente Leonardo Paucar , PINTO, Mauro Sergio Silva , CUNHA, António Manuel Trigueiros da Silva
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Maranhão
• Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA DE ELETRICIDADE/CCET
• Departamento: DEPARTAMENTO DE ENGENHARIA DA ELETRICIDADE/CCET
• País: Brasil
• Palavras-chave em Português: aprendizado de máquina; nó sensor; poluentes em ambientes aquáticos,; rede de sensores sem fio; sistemas embarcados;
• Palavras-chave em Inglês: machine learning; sensor node; pollutants in aquatic environments; wireless sensor network; embedded systems.
• Área do conhecimento CNPq: Engenharia Elétrica
• Link de acesso: https://tedebc.ufma.br/jspui/handle/tede/5447

To prevent and mitigate the environmental impact of transporting and extracting oil and its derivatives, the conception, design, development and implementation of a sensor node for wireless sensor networks (WSN) is presented in this Thesis. The proposed system is a static intelligent sensor node that detects and classi es pollutants in aquatic environments using machine learning and IoT (Internet of Things) approaches. The development of the sensor node consists of three phases. In the rst phase, the design and modeling of the embedded system includes mathematical modeling of the node, power system, communication structure, detection and classi cation of pollutants via machine learning and IoT. The implementation of the static sensor node is presented in the second phase of the project, which includes functional modeling of the measurement, the architecture of the embedded system and its physical structure. In the last stage, the detection and classi cation tests of the proposed sensor node are performed, including the characterization and implementation of the sensors. The intelligent sensor node is evaluated indoors through the analysis of seawater samples with gasoline and diesel, pH and turbidity measurements of seawater and freshwater with gasoline, and experiments through direct and indirect measurements of seawater and diesel. Due to the facts of the experiments have shown satisfactory results, the proposed sensor node is considered a promising device for detecting and classifying pollutants in real-world aquatic environments.