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The development of home surveillance systems has attracted more and more research in recent years. The development of Internet of Things (IoT) techniques allows to develop the home surveillance system efficiently. Moreover, the machine learning techniques are useful to analyze data collected by IoT systems. The paper presents an implementation of a home surveillance system using IoT and machine learning models. Firstly, we develop the Arduino Uno R3 circuit with five sensors to collect data of home environment (temperature, humidity, gas, noise and light). Then, we apply various machine learning models (e.g., K-nearest neighbor, support vector machine and neural network) to analyze and predict collected data. Finally, the abnormal values are detected to inform to users efficiently. The use of various machine learning models improves the ability of analysis of home surveillance system based on IoT systems.

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An Implementation of Smart Home Surveillance System Using Internet of Things (IoT) and Machine Learning Models

  • 会议论文

  • 发表日期：

    2025年

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  • Bui Hai Phong,
  • Hoang Xuan Hien,
  • Phuong Anh Nguyen,
  • Tran Nam Tran,
  • Huynh Minh Tri,
  • Nguyen Ngoc Duy,
  • Pham Van Khuong,
  • Kieu Viet Gia Huy,
  • Le Anh Ngoc

摘要

The development of home surveillance systems has attracted more and more research in recent years. The development of Internet of Things (IoT) techniques allows to develop the home surveillance system efficiently. Moreover, the machine learning techniques are useful to analyze data collected by IoT systems. The paper presents an implementation of a home surveillance system using IoT and machine learning models. Firstly, we develop the Arduino Uno R3 circuit with five sensors to collect data of home environment (temperature, humidity, gas, noise and light). Then, we apply various machine learning models (e.g., K-nearest neighbor, support vector machine and neural network) to analyze and predict collected data. Finally, the abnormal values are detected to inform to users efficiently. The use of various machine learning models improves the ability of analysis of home surveillance system based on IoT systems.