Smart environments demand performance monitoring and self-adaptation strategies to maintain efficiency under varying operational conditions. This article presents a systematic literature review on monitoring and adaptation methods for performance metrics in smart environments, focusing on approaches that utilize artificial intelligence, machine learning, and control theories. The research included 142 studies, selected from an initial set of 7,772 articles sourced from seven databases, providing insights into applied techniques, monitored metrics, and challenges encountered. The article proposes five taxonomies: one to categorize monitoring techniques, another for self-adaptation techniques, a third focused on different types of smart environments, a fourth taxonomy for monitored performance metrics, and a final one organizing the main challenges and bottlenecks faced in implementing these solutions. The results underscore the importance of self-adaptation and highlight gaps for developing more resilient and scalable solutions. Thus, this review contributes by mapping current advancements and limitations, supporting future research, and improving technologies applicable to smart environments.

This research was partially financed by national funds through FCT - Foundation for Science and Technology, I.P. under projects UIDB/04466/2025 and UIDP/04466/2025. And, project 16881, LISBOA2030-FEDER-00816400.

• Computer and Information Sciences - Natural Sciences
• Other Engineering and Technology Sciences - Engineering and Technology
• Electrical Engineering, Electronic Engineering, Information Engineering - Engineering and Technology