Energy Efficiency in Wireless Sensor Networks
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Date
2025-01-21
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Tassadit
Abstract
Wireless Sensor Networks (WSNs) face significant challenges in terms of energy efficiency, requiring strategies that balance power consumption, data quality, and network reliability to maximize network lifetime. This issue becomes more complex with the growing adop tion of IoT applications and the dynamic nature of sensor deployment environments. In this context, energy-efficient solutions for WSNs have gained prominence, especially those incorporating machine learning and graph-based techniques.
This thesis investigates energy optimization strategies for Wireless Sensor Networks (WSNs) in the context of IoT applications and dynamic deployment environments. We introduce Delta-GCN, a novel model that combines Graph Convolutional Networks (GCNs) with a customized Long Short-Term Memory (LSTM) layer to capture spatial and temporal dependencies in sensor networks. The model is integrated into a Federated Learning (FL) framework, enabling decentralized training while preserving data privacy.
Our semi-supervised approach addresses the challenge of missing data in WSNs through data imputation, simultaneously optimizing energy consumption by reducing unnecessary sensor activity. Extensive empirical evaluations demonstrate that Delta-GCN significantly outperforms existing state-of-the-art methods across key performance metrics, including F2 score, precision, recall, energy consumption, and network lifetime. The research find ings underscore the potential of integrating GCNs and FL for developing scalable, energy efficient WSN deployments. This work contributes to the field by advancing energy man agement strategies applicable to real-world sensor networks, paving the way for more sus tainable and efficient IoT infrastructures.
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Keywords
Wireless Sensor Networks, Energy Optimization, Graph Convolutional Networks, Federated Learning, IoT