An Energy-Efficient Cross-Layer Clustering Approach Based on Gini Index Theory for WSNs

Energy efficiency is critical in Wireless Sensor Networks (WSNs) due to the limited power supply. While clustering algorithms are commonly used to extend network lifetime, most of them focus on single-layer optimization. To this end, an Energy-efficient Cross-layer Clustering approach based on the Gini (ECCG) index theory was proposed in this paper. Specifically, a novel mechanism of Gini Index theory-based energy-efficient Cluster Head Election (GICHE) is presented based on the Gini Index and the expected energy distribution to achieve balanced energy consumption among different clusters. In addition, to improve inter-cluster energy efficiency, a Queue synchronous Media Access Control (QMAC) protocol is proposed to reduce intra-cluster communication overhead. Finally, extensive simulations have been conducted to evaluate the effectiveness of ECCG. Simulation results show that ECCG achieves 50.6% longer the time until the First Node Dies (FND) rounds, up to 30% lower energy consumption compared with Low-Energy Adaptive Clustering Hierarchy (LEACH), and higher throughput under different traffic loads, thereby validating its effectiveness in improving energy efficiency and prolonging the network lifetime.