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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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A Clustering Scheme Based on the Binary Whale Optimization Algorithm in FANET.

Yonghang Yan1,2, Xuewen Xia1, Lingli Zhang3

  • 1School of Computer and Information Engineering, Henan University, Kaifeng 475004, China.

Entropy (Basel, Switzerland)
|July 8, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel clustering scheme for Unmanned Aerial Vehicle (UAV) networks using the binary whale optimization algorithm (BWOA). The BWOA-based approach enhances energy efficiency and network lifetime in flying ad hoc networks (FANETs).

Keywords:
FANETUAV clustersbinary whale optimization algorithm (BWOA)clustering

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Unmanned Aerial Vehicles (UAVs) are increasingly utilized in diverse applications, leading to the development of multi-UAV networks, also known as flying ad hoc networks (FANETs).
  • Effective management of FANETs through clustering is crucial for optimizing energy consumption, extending network lifespan, and improving scalability.
  • The inherent limitations of UAVs, such as restricted energy and high mobility, pose significant challenges for robust cluster communication networking.

Purpose of the Study:

  • To propose an efficient clustering scheme for UAV clusters utilizing the binary whale optimization algorithm (BWOA).
  • To address the challenges of energy efficiency and network lifetime in mobile UAV networks.
  • To enhance the scalability and communication networking capabilities of FANETs.

Main Methods:

  • Calculating the optimal number of clusters based on network bandwidth and node coverage constraints.
  • Employing the binary whale optimization algorithm (BWOA) for selecting cluster heads.
  • Implementing a distance-based clustering approach for dividing UAVs into groups.
  • Establishing a cluster maintenance strategy for sustained network efficiency.

Main Results:

  • The proposed BWOA-based clustering scheme demonstrates superior performance compared to existing Binary Particle Swarm Optimization (BPSO) and K-means algorithms.
  • Significant improvements were observed in terms of reduced energy consumption.
  • The scheme effectively enhances the overall network lifetime of UAV clusters.

Conclusions:

  • The binary whale optimization algorithm provides an effective solution for clustering in UAV networks.
  • The proposed scheme offers a promising approach for managing FANETs, balancing energy efficiency and network longevity.
  • This research contributes to the advancement of scalable and sustainable multi-UAV communication systems.