Abstract: The rapid development of the Internet of Things (IoT) has highlighted the importance of Wi-Fi sensor networks in efficiently collecting data anytime and anywhere. This paper aims to propose an optimized routing protocol that significantly reduces power consumption in IoT systems based on clustering algorithms. The paper begins by introducing the architecture of Wi-Fi sensor networks, sensor nodes, and the key technologies needed for implementation. It distinguishes between cluster-based and planar protocols, noting the advantages of each. The proposed protocol, DKBDCERP (Dual-layer K-means and Density-based Clustering Energy-efficient Routing Protocol), utilizes a two-layer clustering approach. In the first layer, nodes are clustered based on density, while in the second layer, first-level cluster heads are further grouped using the K-Means algorithm. This dual-layer structure balances the responsibilities of cluster heads, ensuring a more efficient distribution of data reception, fusion, and forwarding tasks across different levels. Simulation results demonstrate that the DKBDCERP protocol achieves optimal performance, with the smallest curve value and the most stable amplitude. It significantly reduces energy consumption, with the total cluster-head power consumption recorded at 0.1J and a variance of 0.1×10⁻⁴. The introduction of two election modes during the clustering stage and the adoption of a centralized control mechanism further contribute to reduced broadcast energy loss. This research introduces an innovative two-layer clustering scheme that enhances the energy efficiency of wireless sensor networks in IoT environments. By leveraging clustering algorithms and a network routing protocol optimized through big data mining techniques, the proposed DKBDCERP significantly reduces energy consumption while maintaining communication stability in large- scale wireless Internet of Things (IoT) systems. The optimized routing protocol provides a novel solution for reducing power consumption while maintaining network stability, offering valuable insights for future IoT applications.

Jing Guo. “Wireless Internet of Things System Optimization Based on Clustering Algorithm in Big Data Mining”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.4 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0160416

@article{Guo2025,
title = {Wireless Internet of Things System Optimization Based on Clustering Algorithm in Big Data Mining},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160416},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160416},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {4},
author = {Jing Guo}
}