  Human existence is getting more sophisticated and better in many areas due to
remarkable advances in the fields of automation. Automated systems are favored
over manual ones in the current environment. Home Automation is becoming more
popular in this scenario, as people are drawn to the concept of a home
environment that can automatically satisfy users' requirements. The key
challenges in an intelligent home are intelligent decision making,
location-aware service, and compatibility for all users of different ages and
physical conditions. Existing solutions address just one or two of these
challenges, but smart home automation that is robust, intelligent,
location-aware, and predictive is needed to satisfy the user's demand. This
paper presents a location-aware intelligent RSSI-based home automation system
(IRHA) that uses Wi-Fi signals to detect the user's location and control the
appliances automatically. The fingerprinting method is used to map the Wi-Fi
signals for different rooms, and the machine learning method, such as Decision
Tree, is used to classify the signals for different rooms. The machine learning
models are then implemented in the ESP32 microcontroller board to classify the
rooms based on the real-time Wi-Fi signal, and then the result is sent to the
main control board through the ESP32 MAC communication protocol to control the
appliances automatically. The proposed method has achieved 97% accuracy in
classifying the users' location.
