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

TL;DR

This paper proposes a secure, efficient UAV communication routing framework combining SDN and blockchain, utilizing reinforcement learning to adaptively select high-security paths and improve robustness against attacks.

Contribution

It introduces a novel security metric, a combined beam search and PPO algorithm, and demonstrates superior performance in secure, low-latency UAV routing under attack scenarios.

Findings

BSPPO outperforms PPO, BS-Q learning, and BS-actor critic in delay and energy efficiency.

The architecture ensures high security and robustness in UAV networks.

Simulation results show improved transmission success rate under attack conditions.

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.