uploaded to the cloud, and data analysis and decision making can be performed by machine learning algorithms both as supervised and unsuper- vised learning algorithms. In Figure 2.1, the 5G communication system architecture is shown, where data from the various sensors is collected, and passed through the local processing unit (LPU) to the body sensor network (BSN) server via an access point [7, 8]. The various sensors are installed for determining electroencephalogram (EEG), electrocardio- gram (ECG), and electromyogram (EMG) signals and for the measurement of blood pressure (BP), temperature and motion of the patient. The collected data from the sensors is transmitted to a smart phone or microcontroller, which acts as a local pro- cessing unit (LPU). The processed data is further sent to a BSN cloud server via an access point such as a Wi-Fi router or base transceiver station (BTS). In a BSN server, stored data can be analysed using machine learning algorithms such as linear discrim- inant analysis (LDA) and principal component analysis (PCA) [9] or real-time data can also be live streamed on the Thing Speak Cloud or through a smartphone app such as Blynk. The following steps are used in LDA: Step1: The scaled data point is defined as: Variable_N = (existing variable-average of all variables)/ (standard deviation of all variables). Step 2: Generate a covariance matrix. Step 3: Find eigenvalues and eigenvectors. Step 4: Calculate the principal components. Local Processing Access Health Checks Point Server Temperature Step Count Joint movements FIGURE 2.1 IoT-based 5G communication architecture for body sensor network. 5G Wireless in Healthcare Informatics This algorithm generates small data from the large data set without loss of important features. For a secure healthcare system, a secure social IoT health framework with a physical layer cryptography scheme is proposed [10]. The proposed scheme is for BSN applications. In [11], medical certificates are issued by the IoT based healthcare system using blockchain 