Transactions on Machine Intelligence 2821-1693 6 4 2023 10 01 Optimal Routing-Clustering Aware of Energy Consumption in Wireless Sensor Networks based on Deep Tree Learning 236 247 186331 10.47176/TMI.2023.236 EN B. Saleh Department of Information Technology, Sabzevar Branch, Islamic Azad University, Sabzevar, Iran 0000-0002-3318-397X A. A. Neghabi Ph.D., Department of Computer Engineering, Sabzevar Branch, Islamic Azad University, Sabzevar, Iran 0000-0003-2147-7441 Journal Article 2023 06 02 Presently, the application of Wireless Sensor Networks (WSNs) poses challenges across various domains, with the most prominent being the energy consumption of sensor batteries. Sensor nodes, dispersed in diverse geographical environments for their designated purposes, rely on batteries for data collection. The deployment of sensor nodes induces energy losses during data collection and transmission, particularly in routing data, which demands substantial energy. To tackle this issue, clustering is employed either before or concurrently with routing. This article explores the implementation of clustering-routing alongside sleep and wake scheduling in sensor nodes to effectively conserve energy. The study introduces the optimal OCADR (Constrained Anisotropic Diffusion Routing) protocol, enhancing it with the DAVL (Deep Adelson-Velskii and Landis) tree rotation clustering algorithm. The research reveals that this innovative approach offers improved scheduling in terms of sensor nodes' sleep and wake time compared to prior methods. Moreover, it efficiently transmits packets to the base station through the head clusters. The initial energy allocation was 50 Joules, and after simulation using this method, only 22 Joules were consumed, leaving 28 Joules for network survival an advancement surpassing earlier methodologies. https://www.tmachineintelligence.ir/article_186331_35ee912d84159229f36e445ceab59063.pdf