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

Today due to using Wireless Sensor Networks (WSNs) in many application, some challenges are facing in these kinds of networks. The most important challenge is battery in sensor which defined as energy consumption. Sensor nodes located and employed in geographical environment as their application and they use battery because of collecting data. Setting up sensor nodes and distribute them in an environment will lost at the beginning and also in data collection and transmitting some other energy will be use. Transmitting means data routing in network and it will use many energy. To cover this problem, clustering will be used before or simultaneous of routing. This article use clustering-routing by sleep and wake scheduling in sensor nodes to save energy. For this purpose, this research presents an optimal Constrained Anisotropic Diffusion Routing (CADR) protocol called OCADR and optimizes it with Deep Adelson-Velskii and Landis (DAVL) tree rotation clustering algorithm. The results represented that the proposed approach can perform a proper scheduling in comparison to previous methods in the sensor node’s sleep and wake time and can also transmit packets to the base station through the head clusters. The total of ۵۰ Joules energy defined at first and after using this method in simulation, only ۲۲ Joules of energy are consumed which ۲۸ Joules remain for the survival of the network that is an improvement over previous methods.

Keywords:

Wireless Sensor Networks (WSNs) , Constrained Anisotropic Diffusion Routing (CADR) , Sleep and Awake Scheduling , Clustering , Velskii and Landis (AVL) Tree , Clustering , Routing , Deep Learning