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.
L. Jaison, C. Helen Sulochana
St. Xavier's Catholic College of Engineering, India
Sensor Networks, Clustering, Routing, Sailfish Optimization Algorithm, Deep Neural Networks
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| Published By :
ICTACT
Published In :
ICTACT Journal on Communication Technology
( Volume: 16 , Issue: 1 , Pages: 3475 - 3483 )
Date of Publication :
March 2025
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41
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