Synchronisation, optimisation, and adaptation of machine learning techniques for computer vision in cyber-physical systems: a comprehensive analysis

Cyber-Physical Systems (CPS) seamlessly integrate computers, networks, and physical devices, enabling machines to communicate, process data, and respond to real-world conditions in real time. By bridging the digital and physical worlds, CPS ensures operations that are efficient, safe, innovative, and controllable. As smart cities and autonomous machines become more prevalent, understanding CPS is crucial for driving future progress. Recent advancements in edge computing, AI-driven vision, and collaborative systems have significantly enhanced CPS capabilities. Synchronisation, optimisation, and adaptation are intricate processes that impact CPS performance across different domains. Therefore, identifying emerging trends and uncovering research gaps is essential to highlight areas that require further investigation and improvement. This systematic review and analysis aims to offer a unique point to researchers and facilitates this process by allowing researchers to benchmark and compare various techniques, evaluate their effectiveness, and establish best practices. It provides evidence-based insights into optimal strategies for implementation while addressing potential trade-offs in performance, resource usage, and reliability. Additionally, such reviews help identify widely accepted standards and frameworks, contributing to the development of standardised approaches.