Author:
Krishna K. Phani Rama,Thirumuru Ramakrishna
Abstract
Wireless sensor networks (WSNs) have recently acquired prominence in a variety of applications such as remote monitoring and tracking. Since it is virtually hard to recharge the nodes in their remote deployment, also, the transmission of data from nodes to the base station requires a significant amount of energy. Thus, our research proposes a routing protocol, namely hybrid falcon-improved ACO Nature-Inspired Optimization using a deep learning model to reduce energy consumption while increases the network lifetime. In the developed model, initially, the falcon optimization technique is utilized to locate the best possible cluster heads in the quickest possible time. Furthermore, to improve the quality of service in routing optimization a new improved ACO has been proposed in which linear flexible operator and the premier operator are used to increasing the iteration speed. Finally, the optimum route is obtained through DBNN based on predicted energy. As a result, our proposed model gives a lifetime as 121 s and energy consumption as 0.041 J at 500 rounds when compared to the baseline approaches. Therefore, our proposed approaches provides better routing and improves the QoS as well as the energy consumption which increases the longevity of mobile nodes.
Publisher
Czech Technical University in Prague - Central Library
Subject
Artificial Intelligence,Hardware and Architecture,General Neuroscience,Software
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献