Dynamic Routing Mechanism for Load Distribution in UAV Swarm Networks with Edge Caching

The rapid advancement of the UAV swarm network has made its widespread application across a multitude of domains. However, the inherently dynamic nature of the network often gives rise to intermittent connectivity issues, leading to a significant reduction in the data transmission capacity. To address this challenge, this study explores the integration of Information-centric Network (ICN) with the delay-tolerant network (DTN). This design aims to enhance message delivery rates by caching content data packets in UAV nodes. Building upon this architecture, we study the congestion control and load balancing problem. We design an on-demand collaborative communication routing algorithm. In our design, we first propose a routing decision model that incorporates multiple routing metrics to capture the dynamic evolution patterns of network nodes, effectively controlling local congestion issues. Subsequently, we employ Lyapunov optimization techniques to achieve a network load balancing. By integrating the Lyapunov drift function, we ensure the stability of a feasible solution space within the model. Additionally, considering the high communication overhead caused by the sparse communication characteristics of DTN, we deploy a Multi-Agent Incentivized Communication (MAIC) algorithm to optimize routing scheduling strategies. Within the MAIC framework, each agent develops unique models for its teammates to generate customized information and minimize network information redundancy. Simulation results demonstrate that this algorithm effectively ensures a congestion control and a load balancing within the UAV swarm network while maintaining communication overhead in routing computations at a minimal level.