Intelligence Artificielle et Data Sciences

Permanent URI for this community

https://dspace.estin.dz/handle/123456789/3

Browse

Browsing Intelligence Artificielle et Data Sciences by Author "BOUTAOUI Nada"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Energy Efficiency in Wireless Sensor Networks
    (Tassadit, 2025-01-21) BOUTAOUI Nada
    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.
  • Thumbnail Image
    Publication
    Literature Review on energy Efficiency Wireless Sensor Net Works
    (Tassadit, 2025-01-25) BOUTAOUI Nada
    Energy efficiency in WSNs is a problem of resource optimization among sensor nodes that needs to meet the operational constraints for maximum network lifetime and performance. In essence, achieving optimal energy efficiency in WSNs means bing capable of balancing power consumptions, quality of data, and reliability of networks within various deployment scenarios. This issue has become increasingly complicated due to the rapid growth of IoT applications, heterogeneous sensor technologies, and dynamic operation environments. Because of this, it has become a topic of top priority for researchers who are working on the development of proficient methods so that energy management can be done easily and effectively by WSNs operators and designers. The present research work includes a bibliographic study on different approaches followed in WSN energy optimization, focusing on the most recent advances that have incorporated ML and Graph-based techniques. In the following, we will talk about some classic energy-saving approaches, modern ML techniques including Reinforcement Learning (RL), and the potential of Graph Convolutional Networks (GCNs) and Federated Learning (FL) in addressing energy efficiency challenges in WSNs.