Open Access

ARTICLE

Metaheuristic Based Data Gathering Scheme for Clustered UAVs in 6G Communication Network

Ahmed S. Almasoud¹, Siwar Ben Haj Hassine², Nadhem NEMRI², Fahd N. Al-Wesabi^2,3, Manar Ahmed Hamza^4,*, Anwer Mustafa Hilal⁴, Abdelwahed Motwakel⁴, Mesfer Al Duhayyim⁵

1 Department of Information Systems, College of Computer and Information Sciences, Prince Sultan University, Saudi Arabia
2 Department of Computer Science, College of Science & Arts at Mahayil, King Khalid University, Saudi Arabia
3 Faculty of Computer and IT, Sana'a University, Sana'a, Yemen
4 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
5 Department of Natural and Applied Sciences, College of Community-Aflaj, Prince Sattam bin Abdulaziz University, Saudi Arabia

* Corresponding Author: Manar Ahmed Hamza. Email:

Computers, Materials & Continua 2022, 71(3), 5311-5325. https://doi.org/10.32604/cmc.2022.024500

Received 20 October 2021; Accepted 22 November 2021; Issue published 14 January 2022

Abstract

The sixth-generation (6G) wireless communication networks are anticipated in integrating aerial, terrestrial, and maritime communication into a robust system to accomplish trustworthy, quick, and low latency needs. It enables to achieve maximum throughput and delay for several applications. Besides, the evolution of 6G leads to the design of unmanned aerial vehicles (UAVs) in providing inexpensive and effective solutions in various application areas such as healthcare, environment monitoring, and so on. In the UAV network, effective data collection with restricted energy capacity poses a major issue to achieving high quality network communication. It can be addressed by the use of clustering techniques for UAVs in 6G networks. In this aspect, this study develops a novel metaheuristic based energy efficient data gathering scheme for clustered unmanned aerial vehicles (MEEDG-CUAV). The proposed MEEDG-CUAV technique intends in partitioning the UAV networks into various clusters and assign a cluster head (CH) to reduce the overall energy utilization. Besides, the quantum chaotic butterfly optimization algorithm (QCBOA) with a fitness function is derived to choose CHs and construct clusters. The experimental validation of the MEEDG-CUAV technique occurs utilizing benchmark dataset and the experimental results highlighted the better performance over the other state of art techniques interms of different measures.

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Keywords

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