Creation of AI-driven smart spaces for enhanced indoor environments – A survey

Abstract

Smart spaces are ubiquitous computing environments that integrate diverse sensing and communication technologies to enhance functionality, optimize energy utilization, and improve user comfort and well-being. The adoption of emerging artificial intelligence (AI) methodologies has led to the development of AI-driven smart spaces, further expanding capabilities through applications such as personalized comfort settings, interactive living spaces, and automation of space systems. These advancements collectively elevate the quality of indoor experiences for users. To systematically examine these developments, we present a comprehensive survey of the foundational components of AI-driven smart spaces, including sensor technologies, data communication protocols, network management and maintenance strategies, and data collection, processing, and analytics. We investigate both traditional machine learning (ML) methods, such as deep learning (DL), and emerging approaches, including transformer networks and large language models (LLMs), highlighting their contributions and potential. We also showcase real-world applications of these technologies and provide insights to guide their continued development. Each section details relevant technologies and methodologies and concludes with an analysis of challenges and limitations, identifying directions for future research.