Application of Deep Learning Techniques for Internet of Things (IoT) Device-Enabled Smart Home Automation with Predictive Modeling

The automation and control of residential buildings have been revolutionized by smart home environments, which heavily rely on Internet of Things (IoT) gadgets. The goal of this project is to optimize energy usage, improve security, and enhance the user experience through the application of deep learning techniques to IoT-enabled smart home automation. Specifically, the focus is on predictive modeling. Using real-time data from IoT sensors, the project intends to design systems that can anticipate user behavior, detect abnormalities, and predict future device states. It does this by leveraging Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) models. Energy savings and enhanced safety can be achieved by proactive modifications to heating, lighting, and security systems made possible by these predictive capabilities. Additional difficulties that the research attempts to resolve include making reliable predictions from a combination of deep learning models, processing massive amounts of high-dimensional IoT data, and making sure the automation system can scale to accommodate a wide variety of smart home setups. The results show that deep learning works well with complicated IoT data streams; the predictive models are quite accurate at recognizing user actions and predicting device states. This research emphasizes the power of deep learning to revolutionize smart home systems that are connected to the internet of things. It might make these systems more efficient, responsive, and flexible to user needs, all while lowering energy usage and improving security.