RoutePPO: eBPF-Based Proximal Policy Optimization for Adaptive Routing in UAV Swarm Networks
2026 International Conference on Smart Applications, Communications and Networking, SmartNets 2026, Rome, İtalya, 7 - 09 Temmuz 2026, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/smartnets69662.2026.11604730
- Basıldığı Şehir: Rome
- Basıldığı Ülke: İtalya
- Anahtar Kelimeler: Adaptive Routing, eBPF, P4, Proximal Policy Optimization, Reinforcement Learning, UAV Swarm Networks
- Gazi Üniversitesi Adresli: Evet
Özet
Unmanned Aerial Vehicle (UAV) swarm networks demand routing protocols that adapt continuously to rapid topology changes, node mobility, and fluctuating link quality. AODV may incur route-discovery overhead after topology changes, while OLSR relies on periodic topology dissemination that may lag behind fast link-quality changes; both can struggle under UAV swarm dynamics. We present RoutePPO, a closedloop adaptive routing framework that couples Proximal Policy Optimization (PPO) with eBPF-based real-time link telemetry and a P4 programmable data plane. RoutePPO-Adapt introduces a Top-K path encoder with fixed-order slot assignment and a 3-step slot-history observation, producing a topology-agnostic 30-dimensional state representation. Training uses a 9-scenario curriculum with anticipatory reward shaping and cosine learningrate decay. Across 15 deterministic routing scenarios, RoutePPOAdapt achieves a mean reward of 0.674-9.2% above the two-path baseline (RoutePPO-Base) - winning 11 of 15 scenarios while reducing latency by 31.6% and packet loss by 37.1%. A kernel-native evaluation (Linux netns + eBPF TC egress) confirms non-zero telemetry counters (0.13-0.27 Mbps), demonstrating end-to-end viability of the eBPF-PPO pipeline.