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

TL;DR

This paper presents RescueChain, a secure, efficient, and intelligent data sharing scheme for UAV-assisted disaster rescue that integrates lightweight blockchain, reputation-based consensus, vehicular fog computing, and deep reinforcement learning.

Contribution

It introduces a novel integrated framework combining blockchain, VFC, and deep RL to enhance security, efficiency, and resource allocation in UAV disaster rescue operations.

Findings

RescueChain accelerates consensus and improves data sharing security.

It enhances offloading efficiency and reduces UAV energy consumption.

The scheme increases user payoffs through optimized resource strategies.

Abstract

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.