The rapid advancement of Internet of Things (IoT) technologies and cloud computing has transformed modern healthcare systems by enabling smart, connected, and real-time patient monitoring solutions. Cloud-assisted IoT healthcare monitoring systems integrate wearable sensors, smart medical devices, wireless communication technologies, and cloud platforms to collect, process, store, and analyze patient health data continuously. These systems improve healthcare quality by providing remote patient monitoring, early disease detection, emergency response, and personalized treatment recommendations. The architecture of cloud-assisted healthcare systems generally includes sensing layers, communication networks, cloud infrastructure, data analytics modules, and application interfaces for healthcare professionals and patients. IoT sensors capture physiological parameters such as heart rate, blood pressure, oxygen saturation, glucose levels, and body temperature, while cloud platforms provide scalable storage, high computational power, and intelligent analytics. Furthermore, artificial intelligence and machine learning techniques enhance predictive healthcare services and decision-making processes. Despite significant advantages such as cost reduction, accessibility, automation, and improved patient outcomes, several challenges remain, including data privacy, cybersecurity threats, interoperability issues, latency, and infrastructure complexity. This study explores the architecture, literature, methodologies, benefits, and limitations of cloud-assisted IoT healthcare monitoring systems and highlights their growing importance in the development of next-generation smart healthcare environments.
