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

Ahmed S Almasoud,Siwar Ben Haj Hassine,Nadhem Nemri,Abdelwahed Motwakel,Mesfer Al Duhayyim,Fahd N Al-Wesabi,Manar Ahmed Hamza,Anwer Mustafa Hilal

doi:10.32604/cmc.2022.024500

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.

Highlights

The cutting edge wireless communication networks towards 6G are imagined to empower intellectual, secure, dependable, and boundless availability [1]
The experimental validation of the MEEDG-CUAV technique takes place using benchmark dataset and the experimental results highlighted the better performance over the other state of art techniques interms of different measures
A MEEDG-CUAV technique is derived for energy efficient data collection in unmanned aerial vehicles (UAVs) enabled 6G networks

Summary

Introduction

The cutting edge wireless communication networks towards 6G are imagined to empower intellectual, secure, dependable, and boundless availability [1]. A few techniques are presented for addressing this localization issue in UAV networks [5]. The vast majority of these techniques, which utilize the distance estimation strategy, depend on bilateration and trilateration Be that as it may, flip ambiguity (FA) is a significant issue in distance-estimation based localization techniques [6]. In multi-UAV systems, clustering is utilized to control the organization of UAVs in the network. The clustering approach addresses the significant distance correspondence issue, expands network versatility, improves network lifetime, and builds the unwavering quality of the whole organization. Pustokhina et al [12] proposed a novel energy-efficient cluster based UAV network with DL based scene classification technique. In DL technique carried out clustering from the UAV networks at regular intervals depending upon graph convolutional network (GCN) framework that utilized data on RSSI and UAV places. The RL is implemented for configuring the routing topology which regards combined immediate energy cost and entire distance cost of communication path

Contribution of the Paper

The Proposed MEEDG-CUAV Technique

Algorithmic Design of QCBOA Technique

Process Involved in the QCBOA Based Clustering Technique

Experimental Validation

Conclusion