A Clustering Scheme Based on the Binary Whale Optimization Algorithm in FANET

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.