HCPMR: A Hierarchically Coordinated Proximal Multi-Hop Routing Scheme for FANETs in Mission-Critical Environments

In mission-critical environments, designing efficient routing protocols for unmanned aerial vehicles (UAVs) is challenging due to limited energy resources, high-speed mobility, and highly dynamic link conditions. This paper proposes a hierarchical routing framework, termed hierarchically coordinated proximal multi-hop routing (HCPMR) scheme, which integrates multi-agent proximal policy optimization (MAPPO) with graph neural networks (GNNs) to enable scalable, stable, and energy-efficient UAV communication. HCPMR organizes UAVs into energy- and stability-aware clusters, where GNN- assisted intra-cluster coordination is performed by cluster leaders, who also manage inter-cluster routing through periodically synchronized exchanges under a centralized training and decentralized execution (CTDE) paradigm. GNN-based embeddings allow UAVs to capture spatial dependencies within their local neighbourhoods, enabling adaptive routing with reduced control overhead, enhanced reliability, and improved energy efficiency. The proposed scheme incorporates a multi-objective reward function to jointly optimize energy consumption, fairness, delivery reliability, and terrain adaptability. Simulation results using NS-3/ns3-gym across urban, desert, and mountainous scenarios demonstrate that HCPMR increases packet delivery ratio by 6.5% compared to coordinated Q-learning-based multi-hop routing (CQMR) and by 16% compared to improved Q-learning–based multi-hop routing (IQMR). Moreover, HCPMR achieves approximately 13% higher residual energy than CQMR and 58% higher than IQMR, while reducing control overhead by 20% and 38.5%, respectively. These results establish HCPMR as an adaptive and efficient routing framework for dynamic FANETs in mission-critical operations, effectively enhancing routing decisions through GNN-driven embeddings and MAPPO-based coordination.