The rapid development of technology has brought about a revolution in healthcare stimulating a wide range of smart and autonomous applications in homes, clinics, surgeries and hospitals. Smart healthcare opens the opportunity for a qualitative advance in the relations between healthcare providers and end-users for the provision of healthcare such as enabling doctors to diagnose remotely while optimizing the accuracy of the diagnosis and maximizing the benefits of treatment by enabling close patient monitoring. This paper presents a comprehensive review of non-invasive vital data acquisition and the Internet of Things in healthcare informatics and thus reports the challenges in healthcare informatics and suggests future work that would lead to solutions to address the open challenges in IoT and non-invasive vital data acquisition. In particular, the conducted review has revealed that there has been a daunting challenge in the development of multi-frequency vital IoT systems, and addressing this issue will help enable the vital IoT node to be reachable by the broker in multiple area ranges. Furthermore, the utilization of multi-camera systems has proven its high potential to increase the accuracy of vital data acquisition, but the implementation of such systems has not been fully developed with unfilled gaps to be bridged. Moreover, the application of deep learning to the real-time analysis of vital data on the node/edge side will enable optimal, instant offline decision making. Finally, the synergistic integration of reliable power management and energy harvesting systems into non-invasive data acquisition has been omitted so far, and the successful implementation of such systems will lead to a smart, robust, sustainable and self-powered healthcare system.

Internet of things (IoT) is one of the modern technologies, developing existing cellular communication networks to the emerging fifth‐generation technology. The traffic modelling of IoT applications is different from the recommended traffic modelling of a Human‐type communication. Numerous unstructured data arise from rapid growth of IoT heterogeneous networks and services. One of challenges occurs by diverse IoT unstructured data is a variety of IoT data characteristics, which increase traffic modelling influences. The study of IoT characteristics has a crucial role in modelling data bursts of IoT use cases. This study proposes an enhanced ON/OFF traffic modelling technique to model IoT data characteristics of diverse applications, especially the IoT smart city. A novel modelling technique categorises and analyses characteristics of IoT smart city use case into five major traffic patterns. In this study, realistic smart home networks were built as a part of IoT smart city in an experimental model. Massive traffic profiles are generated from a pilot according to a proposed IoT smart city architecture model. Various traffic profiles of a pilot are modelled into theoretical models by Easy‐Fit tool. Traffic modelling concept of a novel technique and their five traffic patterns are proven in the pilot results.