Abstract

Security of lives and properties is highly important for enhanced quality living. Smart home automation and its application have received much progress towards convenience, comfort, safety, and home security. With the advances in technology and the Internet of Things (IoT), the home environment has witnessed an improved remote control of appliances, monitoring, and home security over the internet. Several home automation systems have been developed to monitor movements in the home and report to the user. Existing home automation systems detect motion and have surveillance for home security. However, the logical aspect of averting unnecessary or fake notifications is still a major area of challenge. Intelligent response and monitoring make smart home automation efficient. This work presents an intelligent home automation system for controlling home appliances, monitoring environmental factors, and detecting movement in the home and its surroundings. A deep learning model is proposed for motion recognition and classification based on the detected movement patterns. Using a deep learning model, an algorithm is developed to enhance the smart home automation system for intruder detection and forestall the occurrence of false alarms. A human detected by the surveillance camera is classified as an intruder or home occupant based on his walking pattern. The proposed method’s prototype was implemented using an ESP32 camera for surveillance, a PIR motion sensor, an ESP8266 development board, a 5 V four-channel relay module, and a DHT11 temperature and humidity sensor. The environmental conditions measured were evaluated using a mathematical model for the response time to effectively show the accuracy of the DHT sensor for weather monitoring and future prediction. An experimental analysis of human motion patterns was performed using the CNN model to evaluate the classification for the detection of humans. The CNN classification model gave an accuracy of 99.8%.

Highlights

  • Home-based crime attacks, theft, and burglary are on the increase annually

  • A prototype implementation using Internet of Things (IoT) hardware was used to test the functionality of our system

  • The home appliances were interfaced with the relay module to have the appropriate current flow

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Summary

Introduction

Home-based crime attacks, theft, and burglary are on the increase annually. Despite the lockdown and stay-at-home order, several homes were still attacked and burgled in South Africa [1]. Studies have shown that deep learning and machine learning models have been efficiently applied in smart home automation for object detection and recognition, human activity detection, facial recognition, intelligent control of appliances, energy efficiency, home monitoring, safety, and security [5,6,7,8,9,10]. (i) A proposal for a deep learning (CNN) algorithm for intrusion detection in a secured smart home automation environment (ii) A design and development of an Android-based smart home automation system for the control and monitoring of electrical home appliances and environmental conditions. (iii) A prototype implementation of an IoT, smart home system for home control, monitoring, and security (iv) Experimentation of a CNN-based deep learning model for classifying human walking patterns to detect intruders in a smart home environment.

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System Architecture
40: Remotely control the home
22: Save to cloud
Implementation Details
Conclusion and Future Recommendation
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