Wireless Sensor Networks (WSNs) are critical in numerous
applications due to their ability to sense and transmit data. However,
energy limitations of sensor nodes, powered by finite batteries,
significantly impact network longevity. Traditional routing methods
involving multi-hop transmissions and cluster formation can result in
substantial energy consumption, particularly by Cluster Heads (CHs)
involved in data aggregation and transmission. This research addresses
the problem by optimizing energy-efficient routing using a Deep Belief
Network (DBN) with Long Short-Term Memory (LSTM) for routing
and CH selection. A mobile sink moving in a linear path minimizes
energy consumption by reducing cluster formation and promoting
single-hop transmissions. The proposed method utilizes LSTM-based
CH selection to ensure that nodes with the highest residual energy are
chosen, enhancing network lifetime. Experimental results demonstrate
that the proposed method reduces energy consumption by up to 25%
compared to circular path sink movement and multi-hop data
transmission, resulting in a 40% increase in network lifetime.
Performance was evaluated on a 100-node network with varying sink
velocities, achieving an energy efficiency of 15% over traditional models.
P. Sachidhanandam, M. Sakthivel, A. Gomathi, G. Usha
Knowledge Institute of Technology, India
Wireless Sensor Networks, Deep Belief Network, LSTM, Mobile Sink, Energy Efficiency
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| Published By :
ICTACT
Published In :
ICTACT Journal on Communication Technology
( Volume: 15 , Issue: 3 , Pages: 3307 - 3313 )
Date of Publication :
September 2024
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22
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