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In the IoT framework, the network resources are prioritized and intelligently used over a trustworthy and secure transmission channel that is used for applications of the health-care sector. The inputs are acquired from the sensors of the leaf devices in the IoT framework. The acquired inputs are preprocessed efficiently. If there is any high computation process involved, then the leaf device takes assistance from cloud servers at the backend. A large quantity of processes can be performed by backend cloud server and end devices can be advised by the backend cloud server on the preprocessing steps to give priority to the incoming data from the sensor. Data mining and machine language concepts are used at the backend for extracting the signatures from the sensor data. The healthcare interpretations are provided respectively to the captured sensor data. These steps are used by frontend devices for providing healthcare assessment of the patient.

This system can further be extended where a physician can be included at the back end to analyze the patient’s healthcare data for prominent fluctuations. Thus remote diagnosis can be improved and rural medication can also be provided as well, even with the healthcare provider far from the patient. The methodology of the smart healthcare system is shown in Figure 4.4.

Figure 4.4 Methodology of Smart Healthcare System.

In healthcare, low network latency, real-time responses are required. So cloud computing is not suitable in such situations because of its high network latency. Thus edge computing is proposed as a new distributive computing architecture that can perform most of the computations within the IoT edge devices instead of the cloud. In this chapter, the major focus is on combining the concepts of IoT and edge computing and improving the techniques of edge computing in the field of healthcare.