A heuristic deep feature system for energy management in wireless sensor network

The Wireless-Sensor-Network (WSN) has been employed in all digital applications for several purposes like sensing, storing, and sharing information. However, managing energy consumption is more critical because of the movable environment. Several existing models have addressed these energy management issues. Still, those models lack in optimizing the energy usage of the WSN during the collision environment. This has motivated to find the best solution for energy optimization with an intelligent model. So, the present research article aims to develop the novel Buffalo-based Deep Belief Energy Management Framework (BDBEMF) for the WSN application. Initially, the required number of sensor nodes was created then the BDBEMF was designed to monitor the high consumption nodes. In addition, the Low-energy adaptive-clustering-hierarchy protocol has been considered for the communication process. Consequently, the Cluster Head has been selected based on less energy utilization and high-density hubs. The data rate of each node has been measured, and the high leaded data has been shared to work fewer nodes to balance the energy. Finally, the amount of alive and dead nodes was validated with few communication metrics. The presented model has gained maximum throughput and less energy consumption.