IoT Based Smart Home: A Machine Learning Approach

Smart home is slowly but steadily becoming a part of our daily life in today’s world. IoT provides another dimension to it, and this should not be surprising that there are more IoT-connected devices than humans. This paper scrutinized the current state-of-the-art IoT-based smart home system and proposed a new approach using the Machine Learning(ML) technique, so that it is capable of controlling IoT devices automatically and effectively based on its prediction in real life. Synthetic data is generated, and a portion of real-time sensor data is collected to train the system controlling models. Human presence count and different environmental variables like Temperature, Humidity, and Luminosity are the features of the prediction procedure. Besides, the Controlling Levels of the models are the class attributes. The Decision Tree algorithm is implemented to classify the proposed controlling models’ data. On the other hand, Using the cross-validation technique, performance evaluation of the models is measured, illustrating the system capability.