Une Approche Efficace de Clustering pour Améliorer les Performances dans l’IoT, basée sur les Réseaux de Capteurs Sans Fil.

Abstract

This graduation thesis aims to enhance data management in the Internet of Things domain by proposing an adaptive clustering approach for wireless sensor networks (WSN). The main objective is to design, model, and simulate a new clustering algorithm to meet the specific requirements of these networks. To achieve this, the research begins with a critical analysis of the literature concerning existing clustering algorithms in the context of WSN. This step aims to identify gaps and opportunities for improvement. Next, we propose a new clustering algorithm that optimizes the overall network performance by forming clusters and selecting cluster heads optimally through the introduction of weights. Each sensor in the network individually calculates these weights, considering various metrics such as buffer length, remaining energy, and average distance between nodes. The ultimate objective is to reduce the network’s energy consumption, enhance its access efficiency, and increase the data transmission rate among sensors. We conducted a detailed analysis and comprehensive simulation using the MATLAB simulation tool to evaluate the proposed algorithm. The results demonstrate the effectiveness of this approach compared to existing algorithms in the specialized literature.