Machine learning-based IoT: Developing an energy-efficient and balanced clustering routing protocol (EEB-CR) for WSNs

Nguyen Duy Tan, Thi-Thu-Huong Le

Abstract: Wireless sensor networks (WSNs) have become integral to the Internet of Things (IoT), supporting diverse applications such as healthcare, environmental monitoring, intrusion detection, military surveillance, and industrial automation. However, sensor nodes (SNs) in WSNs are constrained by limited computational capabilities and finite energy reserves, making energy efficiency a critical concern for IoT applications deployed over WSN infrastructure. This study proposes an Energy-Efficient and Balanced Cluster-based Routing protocol (EEB-CR) to improve the operational longevity and energy distribution of WSNs. The EEB-CR protocol operates in three systematic phases: balanced cluster formation, cluster head (CH) selection, and energy-aware route discovery. Initially, balanced clusters are formed using an enhanced fuzzy c<math><mi is="true">c</mi></math>-means algorithm integrated with a mechanism to reduce uneven energy usage among SNs. Subsequently, CHs are optimally selected based on local node density, residual energy, and Euclidean distance to the base station (or gateway), and the CH role is periodically rotated among cluster members to promote fairness in energy consumption. In the final phase, the Ford–Fulkerson algorithm is employed to establish both intra- and inter-cluster data transmission paths with the objective of minimizing communication overhead from SNs to the base station (BS). Performance evaluation conducted through NS2 simulations demonstrates that EEB-CR achieves superior energy distribution balance and improved network stability compared to benchmark protocols such as LEACH-C, TEZEM, PECR, and FC-GWO.

External IDs:doi:10.1016/j.jnca.2025.104269