A Hybrid Framework Integrating Deterministic Clustering, Neural Networks, and Energy-Aware Routing for Enhanced Efficiency and Longevity in Wireless Sensor Network

Wireless Sensor Networks (WSNs) have emerged as crucial tools for real-time environmental monitoring through distributed sensor nodes (SNs). However, the operational lifespan of WSNs is significantly constrained by the limited energy resources of SNs. Current energy efficiency strategies, such as cl...

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Bibliographic Details
Published in:Computers, materials & continua Vol. 84; no. 3; pp. 5463 - 5485
Main Authors: Qamar, Muhammad Salman, Munir, Muhammad Fahad
Format: Journal Article
Language:English
Published: Henderson Tech Science Press 2025
Subjects:
Artificial neural networks
Clustering
Data management
Data transmission
Energy consumption
Energy efficiency
Energy management
Energy sources
Environmental monitoring
Life span
Neural networks
Real time
Regularization
Routing (telecommunications)
Sensors
Wireless sensor networks
ISSN:1546-2226, 1546-2218, 1546-2226
Online Access:Get full text
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Summary:Wireless Sensor Networks (WSNs) have emerged as crucial tools for real-time environmental monitoring through distributed sensor nodes (SNs). However, the operational lifespan of WSNs is significantly constrained by the limited energy resources of SNs. Current energy efficiency strategies, such as clustering, multi-hop routing, and data aggregation, face challenges, including uneven energy depletion, high computational demands, and suboptimal cluster head (CH) selection. To address these limitations, this paper proposes a hybrid methodology that optimizes energy consumption (EC) while maintaining network performance. The proposed approach integrates the Low Energy Adaptive Clustering Hierarchy with Deterministic (LEACH-D) protocol using an Artificial Neural Network (ANN) and Bayesian Regularization Algorithm (BRA). LEACH-D improves upon conventional LEACH by ensuring more uniform energy usage across SNs, mitigating inefficiencies from random CH selection. The ANN further enhances CH selection and routing processes, effectively reducing data transmission overhead and idle listening. Simulation results reveal that the LEACH-D-ANN model significantly reduces EC and extends the network’s lifespan compared to existing protocols. This framework offers a promising solution to the energy efficiency challenges in WSNs, paving the way for more sustainable and reliable network deployments.
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ISSN:1546-2226
1546-2218
1546-2226
DOI:10.32604/cmc.2025.064442