Optimized Clustering in WBSNs Using Hybrid Firefly Optimization and K-Means Algorithms

Articles in Press

Document Type : Original Article

Author
Amir AliAbadian
Department of Electrical and Biomedical Engineering, Shomal University
10.22034/jcse.2026.579427.1080
Abstract
Recently, research on wireless body sensor networks (WBSNs) has gained significant attention and now plays a crucial role in patient monitoring. Due to power supply limitations, wireless nodes are typically organized into clusters for energy-efficient communication. To this end, clustering-based and grid-based approaches are commonly used. In the first approach, nodes are grouped into clusters in such a way that one sensor node is selected as the cluster head. In contrast, in the grid-based approach, the network is divided into limited virtual grids, usually managed by the base station. The firefly optimization algorithm (FOA), in combination with the K-Means algorithm, is used to optimally select cluster centers. Subsequently, the performance of the proposed segmentation method is evaluated and compared with existing methods. Simulation results show that the network lifetime improves by at least 7% compared to existing approaches.
Keywords
Wireless Sensor Networks
Wireless Body Sensor Networks
Clustering
Firefly Optimization Algorithm
K-Means
The CSI Journal on Computer Science and Engineering

Articles in Press, Accepted Manuscript
Available Online from 29 June 2026