Abstract

This research aims at incorporating Wireless Sensor Networks (WSNs) with deep learning to enable farmers to receive early notification about their produce. It presents the structured combined model which is used to solve the problem of energy consumption and the problem of reliability of communication in WSNs. The component include are network model, energy consumption model, a cluster head selection using k-medoids algorithm and route optimization using adaptive sail fish (AS) algorithm. To improve the route performance, the framework has both Deep Feedforward Neural Network (DFFNN) and the Buffalo Algorithm (BA). The WSN comprises the Base Station (BS), low- performance (LP) sensors and High-performance (HP) sensors with the HP sensors performing the duty of the Cluster Heads. Energy consumption is assumed proportional with the data transmission distance and path loss; K-Medoids clustering optimizes efficiency of communication and minimizes power utilization. Some of the results of analysis in MATLAB R2023b show that the proposed model provides enhancements in terms of energy efficiency, residual energy, and packet delivery ratio through simulations.

Authors

L. Jaison, C. Helen Sulochana
St. Xavier's Catholic College of Engineering, India

Keywords

Sensor Networks, Clustering, Routing, Sailfish Optimization Algorithm, Deep Neural Networks