Cluster head selection method of multiple UAVs under COVID-19 situation.

Area of Science:

• Robotics and Automation
• Network Engineering
• Operations Research

Background:

• COVID-19 lockdowns necessitate efficient supply delivery using unmanned aerial vehicles (UAVs).
• Clustering in UAV networks aims to optimize resource use and reduce energy loss, but traditional methods face challenges.
• Existing algorithms like k-means and ant colony lead to high energy consumption, cluster head mortality, and reduced network efficiency.

Purpose of the Study:

• To propose a novel cluster head selection algorithm for UAVs to address limitations of current methods.
• To improve the efficiency and lifespan of UAV networks in supply delivery scenarios.
• To reduce energy consumption and enhance cooperation among UAVs.

Main Methods:

• Development of a cluster head selection algorithm for UAVs based on game theory (CHSA).
• Utilization of a mixed game model for selecting cluster heads within divided regions.
• Selection of a representative node to execute the cluster head selection algorithm.

Main Results:

• The CHSA algorithm effectively reduces energy consumption per communication round.
• Experimental comparisons demonstrate that CHSA prolongs the network life cycle of UAVs.
• The proposed algorithm improves overall cooperation efficiency in UAV networks.

Conclusions:

• The game theory-based CHSA algorithm offers a significant improvement over traditional methods for UAV clustering.
• CHSA enhances the sustainability and operational efficiency of UAVs for critical delivery tasks.
• This approach provides a robust solution for managing UAV networks in dynamic and challenging environments.