Advances in User Behavior Prediction for Smart Home Systems Based on IoT and Intelligent Control

Abstract

Smart home systems, as a pivotal application scenario of the Internet of Things (IoT), have elevated user behavior prediction to a core functional pillar. This paper comprehensively reviews the current research status and prospective development trends of user behavior prediction in smart home systems. Firstly, it conducts an in-depth analysis of behavior recognition and prediction methodologies based on sensor data. Additionally, it explores long short-term memory (LSTM) network-based behavior prediction models, which excel at capturing temporal dependencies in user behavior sequences, enabling precise forecasts of subsequent actions such as adjusting lighting or temperature. Then, the paper discusses smart home control strategies, with a focus on the practical application of intelligent control methods. Fuzzy PID control is widely used in temperature regulation due to its strong adaptability to non-linear and uncertain environments, ensuring stable indoor temperature. Neural network PID control demonstrates superior performance in force-control scenarios such as smart door locks and curtain systems, enhancing operational accuracy and responsiveness. Furthermore, it delves into the application of machine learning methods, including decision trees, support vector machines, and deep learning, in optimizing user behavior analysis, device fault diagnosis, and energy management within smart home systems. Finally, the paper systematically analyzes the challenges faced by current smart home systems, such as data privacy and security risks, poor compatibility between heterogeneous devices, and the low accuracy of behavior prediction in complex scenarios. The advancement of smart home systems will further drive the innovation of IoT technology, delivering more intelligent, convenient, and personalized services to users.