An AI-IoT integrated Smart House Automation using Proximal Policy Optimization

IoT (Internet of Things) has become the ecosystems of smart houses, by providing unparalleled efficiency, security, and convenience. Integration of IoT and AI-based techniques has expansively enhanced the SMA (Smart House Automation) concept. This research provides an intellectual AI-enabled smart house automation system using PPO (Proximal Policy Optimization) technique, and reinforcement learning algorithm acknowledged for its efficiency and stability in stable task control. This work also analyses how the proposed algorithm can be employed in gathering system data from IoT sensors for providing insights and allowing automotive movements. It dynamically adapts to energy usage patterns, environmental contexts, and user behaviour for optimizing the function of home applications like heating, cooling, security devices, and lighting. Experimental outcomes show that PPO-enabled smart house automation considerably improves the system’s capability with 98.5% accuracy rate to acquire complex patterns and send proactive, modified automation with less latency. There are four types of threats recognized in this study like energy manipulation, unauthorized access, temperature spoofing, and unusual patterns. This analysis shows the advantages, and challenges of PPO for the future generation of intelligent house environments.