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Fuzzy with Metaheuristics Based Routing for Clustered Wireless Sensor Networks

Ashit Kumar Dutta1,*, Yasser Albagory2, Majed Alsanea3, Abdul Rahaman Wahab Sait4, Hazim Saleh AlRawashdeh5

1 Department of Computer Science and Information Systems, College of Applied Sciences, 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 Department of Computing, University Name: Arabeast Colleges, Riyadh, 11583, Kingdom of Saudi Arabia
4 Department of Archives and Communication, King Faisal University, Al Ahsa, Hofuf, 31982, Kingdom of Saudi Arabia
5 Department of Cyber Security, Onaizah Private Colleges, Unaizah, 51911, Kingdom of Saudi Arabia

* Corresponding Author: Ashit Kumar Dutta. Email: email

Intelligent Automation & Soft Computing 2023, 35(1), 367-380. https://doi.org/10.32604/iasc.2023.027076

Abstract

Wireless sensor network (WSN) plays a vital part in real time tracking and data collection applications. WSN incorporates a set of numerous sensor nodes (SNs) commonly utilized to observe the target region. The SNs operate using an inbuilt battery and it is not easier to replace or charge it. Therefore, proper utilization of available energy in the SNs is essential to prolong the lifetime of the WSN. In this study, an effective Type-II Fuzzy Logic with Butterfly Optimization Based Route Selection (TFL-BOARS) has been developed for clustered WSN. The TFL-BOARS technique intends to optimally select the cluster heads (CHs) and routes in the clustered WSN. Besides, the TFL-BOARS technique incorporates Type-II Fuzzy Logic (T2FL) technique with distinct input parameters namely residual energy (RE), link quality (LKQ), trust level (TRL), inter-cluster distance (ICD) and node degree (NDE) to select CHs and construct clusters. Also, the butterfly optimization algorithm based route selection (BOARS) technique is derived to select optimal set of routes in the WSN. In addition, the BOARS technique has computed a fitness function using three parameters such as communication cost, distance and delay. In order to demonstrate the improved energy effectiveness and prolonged lifetime of the WSN, a wide-ranging simulation analysis was implemented and the experimental results reported the supremacy of the TFL-BOARS technique.

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Cite This Article

APA Style
Dutta, A.K., Albagory, Y., Alsanea, M., Sait, A.R.W., AlRawashdeh, H.S. (2023). Fuzzy with metaheuristics based routing for clustered wireless sensor networks. Intelligent Automation & Soft Computing, 35(1), 367-380. https://doi.org/10.32604/iasc.2023.027076
Vancouver Style
Dutta AK, Albagory Y, Alsanea M, Sait ARW, AlRawashdeh HS. Fuzzy with metaheuristics based routing for clustered wireless sensor networks. Intell Automat Soft Comput . 2023;35(1):367-380 https://doi.org/10.32604/iasc.2023.027076
IEEE Style
A.K. Dutta, Y. Albagory, M. Alsanea, A.R.W. Sait, and H.S. AlRawashdeh "Fuzzy with Metaheuristics Based Routing for Clustered Wireless Sensor Networks," Intell. Automat. Soft Comput. , vol. 35, no. 1, pp. 367-380. 2023. https://doi.org/10.32604/iasc.2023.027076



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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