A Secure and Intelligent Data Sharing Scheme for UAV-Assisted Disaster Rescue

计算机科学 斯塔克伯格竞赛 供应 资源管理(计算) 资源配置 分布式计算 计算机网络 协议(科学) 高效能源利用 计算机安全 医学 替代医学 数学 数理经济学 病理 电气工程 工程类
作者
Yuntao Wang,Zhou Su,Qiuliang Xu,Ruidong Li,Tom H. Luan,Pinghui Wang
出处
期刊:IEEE ACM Transactions on Networking [Institute of Electrical and Electronics Engineers]
卷期号:: 1-17 被引量:2
标识
DOI:10.1109/tnet.2022.3226458
摘要

Unmanned aerial vehicles (UAVs) have the potential to establish flexible and reliable emergency networks in disaster sites when terrestrial communication infrastructures go down. Nevertheless, potential security threats may occur on UAVs during data transmissions due to the untrusted environment and open-access UAV networks. Moreover, UAVs typically have limited battery and computation capacity, making them unaffordable for heavy security provisioning operations when performing complicated rescue tasks. In this paper, we develop RescueChain, a secure and efficient information sharing scheme for UAV-assisted disaster rescue. Specifically, we first implement a lightweight blockchain-based framework to safeguard data sharing under disasters and immutably trace misbehaving entities. A reputation-based consensus protocol is devised to adapt the weakly connected environment with improved consensus efficiency and promoted UAVs’ honest behaviors. Furthermore, we introduce a novel vehicular fog computing (VFC)-based off-chain mechanism by leveraging ground vehicles as moving fog nodes to offload UAVs’ heavy data processing and storage tasks. To offload computational tasks from the UAVs to ground vehicles with idle computing resources, an optimal allocation strategy is developed by choosing payoffs that achieve equilibrium in a Stackelberg game formulation of the allocation problem. For lack of sufficient knowledge on network model parameters and users’ private cost parameters in practical environment, we also design a two-tier deep reinforcement learning-based algorithm to seek the optimal payment and resource strategies of UAVs and vehicles with improved learning efficiency. Simulation results show that RescueChain can effectively accelerate consensus process, improve offloading efficiency, reduce energy consumption, and enhance user payoffs.
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