SDN-Blockchain Based Security Routing for UAV Communication via Reinforcement Learning

Abstract: The unmanned aerial vehicle (UAV) network plays important roles in emergency communications. However, it is challenging to design reliable routing strategies that ensure low latency, energy efficiency, and security in the dynamic and attack-prone environments. To this end, we design a secure routing architecture integrating software-defined networking (SDN) for centralized control and blockchain for tamper-proof trust management. In particular, a novel security degree metric is introduced to quantify the UAV trustworthiness. Based on this architecture, we propose a beam search-proximal policy optimization (BSPPO) algorithm, where beam search (BS) pre-screens the high-security candidate paths, and proximal policy optimization (PPO) performs hop-by-hop routing decisions to support dynamic rerouting upon attack detections. Finally, extensive simulations under varying attack densities, packet sizes, and rerouting events demonstrate that BSPPO outperforms PPO, BS-Q learning, and BS-actor critic in terms of delay, energy consumption, and transmission success rate, showing the outstanding robustness and adaptability.