LEADER ELECTION METHOD FOR IMPROVING COMMUNICATION EFFICIENCY IN UAV SWARMS

Abstract

Unmanned aerial vehicle (UAV) swarms are increasingly employed in tasks requiring high coordination, resilience, and adaptability of communication systems. Ensuring efficient and reliable information exchange among swarm members is a critical challenge, particularly in dynamic environments with varying topology and communication constraints. Traditional leader election approaches in distributed systems rely primarily on static criteria such as energy levels, node degree, or link reliability. However, these methods often overlook the influence of message delivery delay, which directly affects synchronization, stability, and mission performance in UAV swarms.

This paper introduces an enhanced leader election method for UAV swarms that combines classical parameters with a novel temporal criterion: the estimated time required for a candidate leader to deliver messages to all swarm members. By integrating this factor into the utility function, the method accounts for both structural and dynamic characteristics of the swarm network. This approach allows for improved alignment of inter-drone interactions, reduced communication delays, and minimized relay load while maintaining energy efficiency.

The proposed method was evaluated through a series of simulations conducted in ROS 2 (Humble) with Gazebo. Swarms of 10 UAVs were modeled across different formations—line, wedge, and cube—under varying communication ranges. Leader failure scenarios were simulated to test re-election performance. Results demonstrated significant improvements: up to 15% reduction in average and maximum message delivery time and up to 42% decrease in relay load in topologies with limited connectivity. The most notable gains were observed in elongated (line) and constrained (cube with reduced range) formations, while performance improvements were less pronounced in densely connected networks. Importantly, energy consumption of elected leaders remained at a comparable level to baseline methods, confirming the efficiency of the proposed approach.

The study highlights the potential of incorporating temporal delivery metrics into leader election algorithms for UAV swarms. The method enhances communication robustness and coordination efficiency, thereby contributing to safer and more reliable swarm operations. Future research directions include integration with intrusion detection mechanisms and adaptive routing strategies for highly dynamic or adversarial environments.