FedRoute: A Multi-Server Federated Meta-DRL Routing Scheme for Tactical Air-Ground WSNs

Tactical air-ground wireless sensor networks (TAG-WSNs) are mission-critical wireless sensor networks (WSNs) that employ airborne sensor nodes (ASNs) to capture aerial sensor data during military operations, thereby overcoming the sensing coverage limitations of the ground network. However, intelligent jamming attacks on the network’s links, coupled with the highly dynamic network topology, disrupt data communication and pose challenges for reliable routing. In this paper, we introduce a cross-layer (MAC-PHY) jamming framework that models the hostile characteristics of TAG-WSNs. Secondly, we propose a scalable federated deep reinforcement learning (FDRL)-enabled routing solution called FedRoute, which enables agents to build a shared routing model. To support jamming-resilient collaborative model training, we use multiple spatially distributed mobile robot nodes (MRNs) as parameter servers. In FedRoute, local DRL models are meta-trained with the routing agents’ exploration data before federated averaging, resulting in meta-optimized regional routing models. Moreover, FedRoute empowers routing agents to discover quick and reliable routes in the presence of jamming attacks on acknowledgment (ACK), negative acknowledgment (NACK), and data packets. Under cross-layer (MAC-PHY) jamming attacks, the proposed scheme is found to outperform cluster-based trusted routing (CTRF) in terms of expected transmission count (ETX) by 11%, packet delivery ratio (PDR) by 6.5%, and end-to-end (E2E) delay by 14.5%. Furthermore, compared to DQN-routing, the proposed scheme improves ETX by 5.9%, PDR by 5.6%, and E2E delay by 32.5%.