EE-AIRP: An AI-enhanced energy-efficient routing protocol for IoT-enabled WSNs

Abstract

Wireless sensor networks (WSNs) have become essential components in Internet of Things (IoT) applications for data collection and processing. The constrained energy resources and limited computational capacities inherent in sensor nodes impose significant limitations on the operational lifespan of WSNs, thereby constituting a persistent and critical research challenge in the field. This paper proposes an Energy-Efficient Artificial Intelligence-based Routing Protocol (EE-AIRP) to extend network lifespan in WSN-based IoT applications. The proposed methodology offers three principal innovations that collectively advance the current state of research: (1) network zone partitioning using the DBSCAN machine learning algorithm to form approximately balanced clusters based on node distribution density, (2) intelligent cluster head (CH) selection using a multi-criteria fitness function that weighs residual energy, proximity to Sink device, and distribution density, and (3) an optimized path formation strategy for both intra-cluster and inter-cluster data transmission, leveraging an enhanced A*-based routing algorithm to minimize communication overhead and improve energy efficiency. Performance evaluations across three diverse scenarios demonstrate that EE-AIRP–averaged over multiple independent runs–achieves substantial energy-efficiency gains, approximately 40% relative to LEACH-C and 28%, 12%, and 6% compared with H-KDTREE, PECR, and KMSC, respectively. Moreover, the protocol extends network lifetime by promoting a more balanced distribution of energy consumption across sensor nodes than these baseline protocols. These findings–reported with dispersion measures–corroborate the robustness and reproducibility of EE-AIRP under both dense and sparse deployments. These improvements make EE-AIRP particularly suitable for IoT applications such as environmental monitoring, healthcare, and smart buildings, where network longevity is critical. The EE-AIRP code and corresponding simulation results can be found at: https://doi.org/10.5281/zenodo.17149005.