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

2026-01-19

AI Summary

This study addresses the challenge of achieving reliable routing in dynamic and vulnerable unmanned aerial vehicle (UAV) networks while simultaneously optimizing latency, energy efficiency, and security. To this end, the authors propose a novel secure routing architecture that integrates software-defined networking (SDN) with blockchain technology. The approach introduces an innovative trust quantification mechanism based on a security score and employs a hybrid strategy combining beam search with proximal policy optimization (PPO) to enable dynamic secure path selection and rerouting. Experimental results demonstrate that the proposed BSPPO algorithm consistently outperforms baseline methods—including PPO, BS-Q-learning, and BS-Actor-Critic—across varying attack densities and traffic loads, achieving superior performance in terms of latency, energy consumption, and packet delivery success rate, thereby significantly enhancing system robustness and adaptability.

Technology Category

• Planning, Routing, and Scheduling: Planning under Uncertainty
• Search and Optimization: Learning to Search
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Security and Privacy: Large-scale security measurements
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• UAV communication
• secure routing
• low latency
• energy efficiency
• dynamic environments

Innovation

• SDN-Blockchain integration
• security degree metric
• beam search-proximal policy optimization (BSPPO)
• secure UAV routing
• dynamic rerouting