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.
Nowadays, the Multiple Input Multiple Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) is an important method used in wireless communications, especially in 5G cellular communications. As in a wireless network, the input signals pass through a channel, and the input signal undergoes phase shift, attenuation, and interference. So, the password from the user side and the received signals are not the same. Thus, an effective channel estimator is essential to make cellular communication better. Hence, a novel hybrid technique called Chimp-based CatBoost channel estimation (CbCBCE) was proposed. This technique is the combination of the Chimp optimization algorithm and CatBoost algorithm. The channel parameters are estimated and then reduced using the Chimp optimization algorithm. Finally, the proposed model is validated with the case study. Then, the result of the proposed model was estimated, and it was compared with other existing techniques. It is observed that the outcome of the proposed design is more compared with the other conventional methods. The presented model is executed in the MATLAB platform, and it is proved that the proposed model has high throughput, high energy efficiency, less BER, and a high data transfer rate.
Security is the primary concern for everyone. This project is aimed at providing security to homes, banks, museum using robotic eye surveillance system. This project is used to detect the motion using sound technology and provides live streaming when required. Here, a technique of robotic eye surveillance is presented that uses active/passive sound motions to capture and record intruders. This project comprises an eye that mimics a robot eye and a servo motor controlled by Arduino NANO that allow the robotic eye to move. There is of course a sound level measurement and its visualization through a mounted camera inside the eye which is ESP32-CAM, which now acts as an input. And it provides surveillance in 3 directions x, y and z. So, how can the robot know the time it should react? And for what signals it should react? It is decided by time of sounds. The difference is done with the frequencies that are involved and sudden increase of sound intensity. Since 2 microphones are used for direction of sound detection and the continuous sound measurement shows the stepping up of the sound and eventually captures.
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-clusteringhierarchy (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.
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