ABSTRACT
The potential of the Internet of Things (IoT) in revolutionizing the healthcare industry through real-time monitoring and analysis of patient data, remote patient care, and predictive maintenance of medical equipment. It specifically focuses on applications such as remote patient monitoring, early disease detection, and disease prevention, particularly for chronic illnesses. By utilizing IoT technologies, healthcare providers can remotely monitor patients, track vital signs, and deliver personalized care. Federated learning in IoT healthcare leverages distributed devices to train models while ensuring data privacy collaboratively. This approach enables efficient analysis of healthcare data, leading to improved decision-making and personalized patient care. The seamless flow of data facilitated by IoT allows for effective communication and collaboration among stakeholders. This chapter contributes to the exploration of the transformative role of Health IoT (HIoT) in shaping smart healthcare solutions by addressing challenges and trends in the field. It conducts a comprehensive systematic review to analyze the techniques, methods, and tools employed in the HIoT domain. The primary objectives of this review include identifying, classifying, and systematically comparing existing studies that explore the application of IoT in healthcare. The findings of this review provide an in-depth comparison, highlighting both the limitations and potentials identified in the current literature on this subject. This article also discusses how federated learning can assist in the context of the HIoT.
