Optimal Routing-Clustering Aware of Energy Consumption in Wireless Sensor Networks based on Deep Tree Learning

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 ۵۰ Joules, and after simulation using this method, only ۲۲ Joules were consumed, leaving ۲۸ Joules for network survival an advancement surpassing earlier methodologies.

Keywords:

wireless sensor networks (WSNs) , Constrained Anisotropic Diffusion Routing (CADR) , Sleep and Awake Scheduling , Velskii and Landis (AVL) Tree , Clustering , routing , Deep Learning