Open Access

ARTICLE

Chaotic Krill Herd with Fuzzy Based Routing Protocol for Wireless Networks

by Ashit Kumar Dutta^1,*, Yasser Albagory², Farhan M. Obesat³, Anas Waleed Abulfaraj⁴

1 Department of Computer Science and Information Systems, College of Applied Sciences, Univeristy Name: AlMaarefa University, Ad Diriyah, Riyadh, 13713, Kingdom of Saudi Arabia
2 Department of Computer Engineering, College of Computers and Information Technology, Taif University, Taif, 21944, Kingdom of Saudi Arabia
3 Computer Information Systems Department, College of Information Technology and Communication, Tafila Technical University, Tafila, 66110, Jordan
4 Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz Univeristy, Jeddah, 23613, Kingdom of Saudi Arabia

* Corresponding Author: Ashit Kumar Dutta. Email:

Intelligent Automation & Soft Computing 2022, 34(3), 1659-1674. https://doi.org/10.32604/iasc.2022.026263

Received 20 December 2021; Accepted 24 January 2022; Issue published 25 May 2022

Abstract

Energy is considered a valuable source in wireless sensor networks (WSN) for effectively improving the survivability of the network. The non-uniform dispersion of load in the network causes unbalanced energy dissipation which can result in network interruption. The route selection process can be considered as an optimization problem and is solved by utilize of artificial intelligence (AI) techniques. This study introduces an energy efficient chaotic krill herd algorithm with adaptive neuro fuzzy inference system based routing (EECKHA-ANFIS) protocol for WSN. The goal of the EECKHA-ANFIS method is for deriving a better set of routes to destination in such a way as to improve the survivability in the wireless networks. Primarily, the ANFIS model utilizes the models of fuzzy logic and neural networks (NN) to effectively select the relay nodes for energy efficient communication. Besides, a group of fuzzy rules with membership functions (MF) are designed for selecting the next hop node in wireless networks based on distinct input parameters. Moreover, the optimal selection of MF takes place by the use of chaotic krill herd algorithm (CKHA). In order to showcase the improved performance of the EECKHA-ANFIS approach, a series of simulations are implemented and outcomes are inspected under several aspects. The extensive result analysis demonstrates the betterment of the EECKHA-ANFIS technique over the existing techniques interms of different measures.

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Keywords

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