Desenvolvimento de um sistema de localização indoor utilizando machine learning e fingerprinting de Wi-Fi na UFAM: aplicações e desafios
This work proposes the development of an indoor localization system based on machine learning and Wi-Fi fingerprinting at the Federal University of Amazonas (UFAM). The project consists of six blocks, divided into two parts: offline and online. In the offline phase, processes are carried out to capt...
| Autor principal: | Cruz, Cárita Gabriela Monteiro |
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| Grau: | Trabalho de Conclusão de Curso |
| Idioma: | por |
| Publicado em: |
Brasil
2024
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| Assuntos: | |
| Acesso em linha: |
http://riu.ufam.edu.br/handle/prefix/7986 |
| Resumo: |
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This work proposes the development of an indoor localization system based on machine learning and Wi-Fi fingerprinting at the Federal University of Amazonas (UFAM). The project consists of six blocks, divided into two parts: offline and online. In the offline phase, processes are carried out to capture Wi-Fi signals using ESP32 devices, create a database with the collected samples, choose and train a classification model using Support Vector Machine (SVM). In the online phase, the trained model is used to recognize patterns in real-time and determine the expected indoor location. The system aims to overcome the limitations of GPS in indoor environments, such as signal blockages caused by UFAM's infrastructure, providing a precise and efficient solution for locating objects or devices within the university campus. |