In cluster-based remote sensor systems, the non-uniform dispersion of nodes often leads to varying energy consumption levels among clusters. To address this challenge, we propose an effective information aggregation tree, drawing inspiration from prior communication frameworks, to guide clustering design. Our approach incorporates characteristics such as Remaining Controlling, Node Thickness, and Stack Heads of Clusters, leveraging fuzzy logic techniques for their selection. Through inter-cluster steering computations, we adjust energy distribution among cluster heads, aiming to equalize energy consumption across clusters. Furthermore, to mitigate energy usage, we employ data compression techniques on aggregated data, exploiting information relationships. Our evaluation focuses on the effectiveness of these strategies within WSN routing networks, assessing various clustering methods to optimize energy efficiency and enhance network performance.