FedRoute: A Multi-Server Federated Meta-DRL Routing Scheme for Tactical Air-Ground WSNs
Bibliographic record
Abstract
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%.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".