Avaliação de algoritmos de estimativa de posição 3D

• Autor(a) principal: Oliveira, Luis Felipe Borges Morão de
• Data de Publicação: 2022
• Tipo de documento: Trabalho de conclusão de curso
• Idioma: por
• Título da fonte: Repositório Institucional da UFSCAR
• Texto Completo: https://repositorio.ufscar.br/handle/ufscar/16222
• topic: Estimativa da posição; Modelos de propagação; Métodos de trilateração; Agro 4.0; IoT; Received signal strength indicator; OUTROS::ENGENHARIA MECATRONICA

Our society is always increasingly close to a technological environment in which the companies collect and interpret data from their operations. Within this scenario, in parallel with the application of Internet of Things (IoT) and organization of Industry 4.0, the term Agro 4.0 emerged, which refers to the technological revolution that is happening in the farm. In this context, with technological advances, monitoring activities in real-time developed a great importance, because it makes it possible to collect information in real-time about what is happening in the farm and act assertively. To take advantage of the communication system, some communication-based position estimation algorithms can be applied with location sensors, such as GPS, making monitoring more accurate and precise. In this monograph, looking for better methods with high accuracy, precision and execution time of the algorithm, were tested and developed 3D algorithms for estimating the position of the unknown node, which will be based on the Received Signal Strength Indicator (RSSI). Each implementation will use a technology that has been widely applied in the Internet of Things environment: Long Range (LoRa). After obtaining the distances, two trilateration methods will be used and compared. Regarding the scientific progress of the study, we seek to develop a functional prototype for application in industrial maintenance environments in sugar and alcohol plants to verify that the safety instructions are being followed by employees during their work. About the scientific progress of the study, it's expected to develop the best analysis models and generate enough comparisons to produce in the future code that selects the model with performance.