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SimID: Reconocimiento de usuarios de varios dominios basado en Wi-Fi con aprendizaje de similitud de identidad
Zhijian Wang1,2, Lei Ouyang1, Shi Chen1
1School of Software Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Sensors (Basel, Switzerland)
|August 28, 2025
Resumen
SimID ofrece una nueva forma de identificar a las personas que usan señales Wi-Fi, que requieren datos mínimos para un reconocimiento preciso de usuarios en interiores. Este método de preservación de la privacidad funciona incluso en entornos nuevos sin reentrenamiento.
Área de la Ciencia:
- Ciencias de la computación
- Procesamiento de señales
- Aprendizaje automático
Sus antecedentes:
- La identificación de usuarios en interiores mediante Wi-Fi es un campo en crecimiento para hogares inteligentes e IoT.
- Los métodos tradicionales de aprendizaje profundo luchan con la generalización y requieren datos extensos para nuevos escenarios.
Objetivo del estudio:
- Para introducir SimID, un marco de reconocimiento de usuario Wi-Fi de pocos disparos.
- Superar las limitaciones de los clasificadores convencionales de aprendizaje profundo en la identificación basada en Wi-Fi.
Principales métodos:
- SimID emplea el aprendizaje por similitud de identidad, incrustando características de señal específicas del usuario en un espacio de alta dimensión.
- Fomenta una mayor similitud en pares para muestras del mismo individuo.
- Los nuevos usuarios se reconocen al comparar las señales de consulta con un pequeño conjunto de plantillas almacenadas, lo que permite la identificación de pocos disparos sin reentrenamiento.
Principales resultados:
- SimID logra altas precisiones promedio en el conjunto de datos XRF55: 97.53% (acción cruzada), 93.37% (persona cruzada), 92.38% (acción cruzada y persona) y 92.10% (persona cruzada y escena).
- Demuestra robustez en escenarios de pocos disparos, incluidos usuarios invisibles y nuevos patrones de movimiento.
Conclusiones:
- SimID proporciona una solución robusta y eficiente para el reconocimiento de identidad en interiores.
- El marco tiene una promesa significativa para aplicaciones en hogares inteligentes, atención médica y seguridad.
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