A Novel Low-Energy and Low-Latency Clustering Method for Large-Scale Sensor Network

In light of the relentless progress in sensor technology, the proliferation of large-scale wireless sensor Network (LSWSN) has become increasingly prevalent. However, the challenges of energy consumption and data latency have grown more acute within these systems. Addressing these issues, node clustering in LSWSN emerges as a pivotal solution. To tackle the practical industry challenges encountered by LSWSN, this study introduces the Chaotic Elite Prairie Dog Optimization (CEPDO) clustering method. By optimizing the clustering scheme, CEPDO significantly enhances the overall performance of LSWSN. This paper presents an innovative approach that employs a novel chaotic elite optimization strategy, resulting in a notable reduction in energy consumption and effective mitigation of data latency in LSWSN. Furthermore, a novel simulation experiment is devised to faithfully replicate the operational conditions of LSWSN. Comparative evaluations against state-of-the-art research algorithms demonstrate the superior performance of CEPDO in reducing system energy consumption and minimizing data latency.