Centralized Unmanned Aerial Vehicle Mesh Network Placement Scheme: A Multi-Objective Evolutionary Algorithm Approach.

Area of Science:

• Computer Engineering
• Wireless Communications
• Network Optimization

Background:

• Unmanned Aerial Vehicles (UAVs) are transitioning from military to civilian roles, requiring effective communication networks for cooperative tasks.
• Designing robust and efficient communication networks for UAV swarms presents significant challenges.

Purpose of the Study:

• To propose a centralized UAV placement strategy using UAVs as flying access points in a mesh network.
• To optimize UAV geographical placement to cover all ground nodes with minimal UAVs while maximizing data rate fulfillment.

Main Methods:

• Utilizing a Multi-Objective Evolutionary Algorithm (MOEA) for UAV placement optimization.
• Employing the elitist Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to determine optimal UAV positions based on ground node locations and Signal-to-Noise-Ratio (SNR).

Main Results:

• The proposed algorithm effectively optimizes UAV placement, balancing the number of UAVs against ground node data rate requirements.
• Simulation results demonstrate the algorithm's capability to find optimal UAV configurations for coverage and connectivity.

Conclusions:

• The developed centralized UAV placement strategy using NSGA-II provides an efficient solution for establishing reliable communication networks in civilian applications.
• This approach facilitates the deployment of UAVs for diverse tasks requiring seamless connectivity and high data throughput for ground users.