A Secure and Intelligent Data Sharing Scheme for UAV-Assisted Disaster Rescue
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Abstract (arXiv)
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 having 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.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Nov 2022.
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Citing papers per year: 2023: 1; 2024: 0; 2025: 5; 2026: 3.
9 papers.
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- 2026Uncertainty-Guided Label Rebalancing for CPS Safety Monitoringcited by 0
- 2026Uncertainty-Guided Label Rebalancing for CPS Safety Monitoring26 Mar 2026
- 2026Leveraging Large Vision Model for Multi-UAV Co-perception in Low-Altitude Wireless Networks13 Mar 2026
- 2026Blockchain-Enabled Routing for Zero-Trust Low-Altitude Intelligent Networks27 Feb 2026
- 2025Hierarchical Task Offloading and Trajectory Optimization in Low-Altitude Intelligent Networks Via Auction and Diffusion-based MARL5 Dec 2025
- 2025Human-AI Use Patterns for Decision-Making in Disaster Scenarios: A Systematic Review15 Sep 2025
- 2025Trusted Routing for Blockchain-Empowered UAV Networks via Multi-Agent Deep Reinforcement Learning31 Jul 2025
- 2025LLM-Aided Joint Secrecy Precoding and Trajectory for RSMA-Based Heterogeneous UAV Networks23 Jul 2025
- 2025Vision-Based Anti Unmanned Aerial Technology: Opportunities and Challenges14 Jul 2025
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