4.6 Article

LVBS: Lightweight Vehicular Blockchain for Secure Data Sharing in Disaster Rescue

出版社

IEEE COMPUTER SOC
DOI: 10.1109/TDSC.2020.2980255

关键词

Land vehicles; Roads; Voting; Security; Consensus protocol; Complexity theory; Unmanned aerial vehicle (UAV); blockchain; reinforcement learning; data sharing; disaster rescue

资金

  1. NSFC [U1808207, 91746114]
  2. 111 Project
  3. Project of Shanghai Municipal Science and Technology Commission [18510761000]

向作者/读者索取更多资源

This article proposes a lightweight vehicular blockchain-enabled secure (LVBS) data sharing framework for UAV-aided IoV in disaster rescue. The framework utilizes the collaboration between UAVs and blockchain to enable data sharing and secure driving in disaster areas. The research shows that this framework improves the security of the consensus phase and promotes high-quality data sharing.
In disaster areas, a large amount of data (e.g., rescue commands, road damage, and rescue experience) should be delivered among ground rescuing vehicles for safe driving and efficient rescue. When communication infrastructures are destroyed by disasters, unmanned aerial vehicles (UAVs) can be employed to perform immediate rescue missions in destroyed areas and assist data sharing for ground Internet of vehicles (IoV). However, in such UAV-assisted IoV under disaster situation, there exist potential security threats on data sharing among vehicles and UAVs because of the untrusted network environment, unreliable misbehavior tracing, and low-quality shared data. To address these issues, in this article, we develop a lightweight vehicular blockchain-enabled secure (LVBS) data sharing framework in UAV-aided IoV for disaster rescue. First, we propose a novel UAV and blockchain-assisted collaborative aerial-ground network architecture in disaster areas. Second, we develop a credit-based consensus algorithm in the lightweight vehicular blockchain to securely and immutably trace misbehaviors and record data transactions for UAVs and vehicles with improved efficiency and security in reaching consensus. Third, since UAVs and vehicles have little explicit knowledge of the whole network, we develop reinforcement learning-based algorithms to optimally schedule the pricing and quality of data sharing strategies for both data contributor and data consumer via trial and error. Finally, extensive simulations are conducted, which demonstrate that LVBS can effectively improve the security of consensus phase and promote high-quality data sharing.

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