LAUSR

The unmanned aerial vehicle (UAV) swarm systems rely on wireless communications for data sharing and coordination. Recently, both the lazy and eager consensus-based algorithms were proposed to enable swarm-wide data sharing. However, our analysis and experiments show that the performance of both algorithms may degrade drastically in dynamic and heterogeneous network environments. The reason is attributed to the fixed transmitter selection strategies adopted in the algorithms. Therefore, in this paper, we propose a novel adaptive consensus data sharing algorithm by adopting single best transmitter selection to strike a beneficial tradeoff between convergence rate and payload cost. Then, we propose and implement a UAV swarm simulation platform to facilitate simulations in dynamic and heterogeneous environment. Numerical results reveal that the proposed adaptive consensus-based data sharing algorithm performs well across different network scenarios in terms of convergence rate and payload cost.