Design Energy Aware Optimized Grey Wolf Based Recurrent Neural Scheme for Bio Medical Application in Wireless Sensor Platform
Keywords:
Biomedical application, Bio-sensor, Cluster head, Energy consumption, Wireless sensor network, Patient database, InternetAbstract
Nowadays, the wireless-based human health monitoring framework is the most recent trending topic. Furthermore, biomedical sensor networks are used to identify and monitor human health conditions using biomedical applications. In this research, design a novel Grey Wolf based Recurrent Neural Scheme (GWbRNS) for enhancing the performance of WSN in biomedical applications by selecting less energy efficient Cluster Head (CH). Moreover, enhance the performance of patient monitoring with the help of a stored dataset. Initially, the biosensor node was designed to collect the information of the patient. Then update the fitness of the grey wolf in the recurrent network to identify the CH based on the lower energy consumption node. After the selection of CH, identify a secure gateway for forwarding the information to the center server. Finally, the patient information is saved in the patient database which is helpful to the medical staff and healthcare provider for monitoring the condition of people. Therefore, the crafted framework is put into practice using the Python programming language. The achieved results of the developed model are then juxtaposed with those of other established methods, considering factors like energy consumption, latency, power usage, and overall lifespan.
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