IRHA: An Intelligent RSSI based Home automation System

TL;DR

This paper introduces IRHA, a home automation system that uses Wi-Fi RSSI signals and machine learning to accurately detect user location and automatically control appliances, enhancing smart home intelligence.

Contribution

The paper presents a novel location-aware home automation system utilizing Wi-Fi RSSI fingerprinting and decision tree classification implemented on ESP32 microcontrollers.

Findings

Achieved 97% accuracy in user location classification.

Demonstrated effective automatic appliance control based on user location.

Integrated Wi-Fi signal processing with machine learning for smart home automation.

Abstract

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.