Shift invariant deep convolution neural learning for resource efficient healthcare data transmission in WSN

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Wireless Sensor Network (WSN) comprises many small sensors nodes incorporated by IoT which plays vital role in several applications. IoT incorporates physical devices into a network structure that includes software, sensors to collect data from the surrounding environment, and sinks for data transmission. To increase network lifespan during data transmission, a resource-efficient routing system is needed. Different academics have been working on improving routing techniques recently to increase effectiveness and route discovery. The key issue affecting the performance of WSNs is an efficient resource usage of routing. In order to overcome this problem, a new technique called Hierarchical Point wise Mutual Informative Clustering-based Shift Invariant Deep Convolutive Neural Learning (HPMIC-SIDCNL) technique is introduced in WSN. The HPMIC-SIDCNL technique uses the Shift Invariant Convolutive Deep neural learning concept to learn the given input with help of several layers such as input, three hidden layers, and output layer. Initially, the IoT devices are used in sensor nodes for sensing and collecting patient data. In the input layer, sensor nodes are considered as input. Then input is transmitted into first hidden layer where resource of the sensor nodes such as energy and bandwidth are measured. Then estimated resources are transmitted into second hidden layer where the clustering process is carried out using the Hierarchical IBM Point wise Mutual Informative Clustering technique. The Hierarchical IBM clustering uses the Point wise Mutual Information for collection of sensor nodes depend on energy and bandwidth. In the third hidden layer, the cluster is selected on higher residual energy and minimum bandwidth for improving the data transmission and reduces the delay. The cluster head receives the patient data after it has been sent by the source node in that particular cluster. In order to perform data transmission from source to sink node at output layer, cluster then locates the closest cluster head. In this manner, cluster heads are used to execute resource-efficient healthcare data transmission from source to sink node, minimizing the latency. The obtained findings show that the suggested HPMIC-SIDCNL technique outperforms in terms of high delivery ratio with little packet loss and delay. The advantage of the proposed HPMIC-SIDCNL technique, it is simple to understand because it represents a solution to a given problem step by step and is not dependent on any programming language. Since everyone can understand it, even those without programming experience, it is simple.