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Cost-Effective Multi-Type Data Scheduling for Blockchain in Massive Internet of UAVs
Journal article   Peer reviewed

Cost-Effective Multi-Type Data Scheduling for Blockchain in Massive Internet of UAVs

Wenjian Hu, Yao Yu, Yue Zong, Phee Lep Yeoh, Xin Hao, Branka Vucetic and Yonghui Li
IEEE Internet of Things Journal, Vol.11(12), pp.21092-21102
2024

Abstract

Internet of Unmanned Aerial Vehicles (IoUAV) lightweight blockchain architecture cooperative deep reinforcement learning (CDRL)
Whilst blockchain technology holds promise for secure Internet of Things (IoT) data management, its deployment in the massive Internet of Unmanned Aerial Vehicles (IoUAV) still faces significant challenges to satisfy strict requirements for low-latency query services and cost-effective resource consumption. To address these challenges, we present a lightweight multi-type data (MTD) blockchain architecture called LMChain with cost-effective MTD block scheduling. Specifically, LMChain incorporates cross-layer MTD blocks, wherein resource-constrained UAVs retain only lightweight block headers. Block bodies with high query probability are stored in fog nodes, while others are offloaded to cloud storage. Based on the MTD block structure, we develop a cost-effective block scheduling scheme to minimize the overall cost associated with LMChain storage and querying. A cooperative deep reinforcement learning (CDRL) algorithm is designed to efficiently schedule MTD blocks between the fog and cloud layers. Simulation results show that our LMChain significantly reduces the IoUAV blockchain system's storage resource requirements and overall cost while supporting low-latency query services, making it well-suited for massive IoUAV applications. Index Terms—Internet of Unmanned Aerial Vehicles (IoUAV), lightweight blockchain architecture, cost-effective resource consumption , cooperative deep reinforcement learning (CDRL).

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