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Energy was one of the 21st century's most critical components. Electronics and computer technologies have been widely used nowadays to simplify work every day. The crisis and the constant lack of power have popularized and integrated Green Computing algorithms for multidimensional applications on a broad scale. The author attempts to defuse times of crisis related to medical crises using the Green Computing method. Wireless Sensor Networks (WSN) are made up of numerous sensor nodes (SN) that are linked to and play a key part in many applications and the Internet of Things (IoT). To establish a dynamic information exchange network, IoT links physical items like sensors. In a number of areas, the Internet of Things was used. Wearable medical sensors are used to monitor health indicators in patients in this crisis situation. This battery-powered medical sensor has a limited quantity of energy. This is an important hurdle for increased network lifetime. In order to address the current issue effectively, a new technique dubbed Jarvis Patrick regressive resource-efficient gaussian process clustering was suggested to prolong the lifetime of the network. This method is based on the green technology concept that requires the hour. IoT devices are first used in SN to detect and collect patient information. SN is categorized in several clusters using the Jarvis Patrick clustering method after the data collection process. Clustering by Jarvis Patrick is a graphical clustering technique used for dividing SN using the Gaussian process regression function. The regression function reviews the SN and performs the process of grouping based on anticipated bandwidth and energy. The Cluster Head (CH) is selected to enhance data transmission and decrease latency after the clustering process. The information is transmitted to the CH from the source node. Then CH locates the nearest CH using the flight-time method. After then, data are sent through a cluster head from the source node to the sink node. WSN carries out resource-efficient data transmission in this way. Number studies indicate that the REGPRJPC technique successfully enhances the potential and reliability of patient data packets and reduces the incidence of loss and delay.
Srivastava et al. (Wed,) studied this question.