A Heuristic Deep Feature System for Energy Management in Wireless Sensor Network

Abstract

The technology 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 a more critical task because of the movable environment. So, the present research article aims to develop a novel Deep Belief Energy Management Framework (BDBEMF) for the WSN application. Initially, the required number of sensor nodes was created then a book BDBEMF was designed to monitor the high consumption nodes. In addition, the Low-energy adaptive-clustering-hierarchy (LEACH) protocol has been considered to make 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 the maximum throughput and less energy consumption and throughput.

Deep Learning model-based malicious node detection system in wireless multimedia sensor Network

“…WMSN is a modern-day communication tool that can be applied to any technological field. As a result, in a WMSN setting, the setup sensors hub must handle a wide variety of responsibilities, including, but not limited to, location discovery, observation, and data transmission [1]. Wireless multimedia sensor networks (WMSN) provide effective methods in many operational domains.…”