NGUYEN MINH TRI
Research Paper "An Implementation of Smart Home Surveillance System using Internet of Things (IoT) and Machine Learning Models"
Recently, the development of home monitoring systems has become a significant trend, especially with the support of Internet of Things (IoT) technology. These systems offer an efficient solution, allowing users to monitor their living environment and ensure home security easily. With IoT, connecting and collecting data from sensors has never been easier. However, to make these systems work effectively, processing and analyzing large amounts of sensor data is crucial.
In this project, under the guidance of MSc. Bui Hai Phong, I developed a home monitoring system using an Arduino Uno R3 board integrated with five different sensors, including temperature, humidity, gas, noise, and light sensors. These sensors were installed to continuously monitor and collect real-time environmental data, allowing the system to reflect the indoor conditions accurately.
A standout feature of this project is the application of machine learning techniques to process and analyze the sensor data. I used three main machine learning models: K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Neural Networks, to create an intelligent system capable of predicting and detecting environmental anomalies. These models helped classify and quickly detect dangerous situations like gas leaks, extreme temperatures, or unusual noise levels.
During development, I performed data processing and pre-processing to ensure system accuracy. After collecting enough data, the machine learning models were trained to predict abnormal values. When the system detects unusual environmental changes, it sends alerts via a mobile app, helping users respond promptly and appropriately.
Using multiple machine learning models in the system enhanced its ability to analyze data and optimize performance. Each model provides a unique perspective, making the system more flexible in handling unexpected situations. The monitoring system not only delivers accurate data but also offers a safer, smarter experience for users.
This project allowed me to dive deeper into IoT and machine learning and showed me the potential of these technologies in improving everyday life. I believe advanced monitoring systems like this will become increasingly popular and play a crucial role in protecting homes, giving users peace of mind in the modern world.
