Deep Learning Classifier in Wireless Sensor Network

Abstract

Data collection and transmission are both made possible for sensor nodes in a wireless sensor network by wireless transceivers. The utilization of sensing energy has been improved so that WSN operations can continue for longer. The majority of network energy loss is caused by activities such as idle waiting, conflict, packet overflow, and overhearing. Collisions and computations based on conflict might waste energy. This problem can be remedied by TDMA (Time Division Multiple Access) through the scheduling of hub broadcasts. It is necessary to find solutions to the problems with the channel jobs to keep communications going. The Energy Efficient Dynamic Scheduling (EDS-MAC) protocol is proposed for use in wireless sensor networks in this research. The time slots were allotted in an automated fashion, and the channel and control packet headers had abbreviated names. IH-MAC conducts simulations of proposed methods. Clustering helps to speed up a network while simultaneously extending its lifespan. Transmission and link scheduling are handled by CSMA and TDMA in the current iteration of IHMAC. Increased Intelligent Hybrid Multiple Access Control places restrictions on the data transmission capabilities of wireless sensor network nodes. This is made possible by combining the superior qualities of the Q-MAC and Z-MAC protocols.