Generative AI-Based Autonomous Orchestration for Intelligent IoT-Driven Smart Home Systems

Abstract

The rapid proliferation of Internet of Things (IoT) technologies has transformed conventional smart homes into highly interconnected digital environments. However, many existing smart home solutions rely on rule-based automation and reactive control mechanisms, limiting their adaptability to dynamic user behaviors and environmental conditions. This paper suggests an autonomous orchestration framework for intelligent IoT-driven smart home ecosystems that is supported by Generative Artificial Intelligence (GenAI) in order to address these issues. In order to provide real-time system optimization, personalized service orchestration, and predictive automation, the suggested design incorporates generative AI models into an edge-cloud collaborative infrastructure. To simulate occupant behavior and environmental dynamics, multimodal data gathered from wearables, smart appliances, IoT sensors, and environmental monitoring systems is used. While the cloud layer enables extensive model training and ongoing knowledge improvement, a lightweight generative AI module installed at the edge handles context inference, anomaly detection, and short-term decision generation. For energy management, security monitoring, climate regulation, and appliance scheduling, the system automatically creates optimal control rules. Furthermore, operational methods are dynamically updated by reinforcement learning techniques in response to changes in the environment and user preferences. When compared to traditional smart home automation systems, experimental evaluations show that the suggested approach enhances energy economy, reaction latency, personalization accuracy, and flexibility. The suggested framework provides a scalable and safe architecture for next-generation intelligent residential infrastructures, advancing the development of self-optimizing, human-centric smart home ecosystems.