Résumé:
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.
