Distributed Pairwise Protection for Security-Aware Mission Chains in UAV Networks
Yuhang Zhao, Guangming Duan, Danyang Zheng, Xiaojun Cao, Chengzong Peng · 2025
The unmanned aerial vehicles (UAVs) communication network exhibits significant potential in natural disaster management, with applications in flood relief and wildfire control. In these scenarios, UAVs can dynamically form mission chains (MCs) to collaboratively execute tasks such as real-time monitoring and post-disaster search and rescue. To address security challenges in these dynamic environments, we implement MCs as security-aware service function chains (SFCs). However, traditional SFC techniques are often inefficient and resource-intensive when applied to MCs in UAV networks, due to the networks’ dynamic nature and resource constraints. In this paper, we introduce and mathematically formulate a novel problem, termed the security-aware SFC distributed pairwise protection (SSFC-DPP) problem in UAV networks, which optimizes SFC protection against failures while balancing security and resource demands, and prove its NP-hardness. To tackle SSFC-DPP, we propose an efficient heuristic approach, the distributed pairwise node protection (DPNP) algorithm, integrating a security-resource ratio (SRR) factor and pairwise backup selection (PBS) technique. Extensive simulations show that DPNP reduces overall backup costs by 8.05% and 51.39% compared to two benchmark algorithms, respectively.