论文标题
激励利用无人机辅助物联网中有安全数据收集的综合区块链:一种多代理增强学习方法
Incentivizing Proof-of-Stake Blockchain for Secured Data Collection in UAV-Assisted IoT: A Multi-Agent Reinforcement Learning Approach
论文作者
论文摘要
物联网(物联网)可以方便地部署,同时赋予各种应用程序,在该应用程序中,物联网节点可以在其中形成群集以共同完成某些任务。在本文中,我们建议使用无人驾驶飞机(UAV)来协助基于区块链的安全性供应的聚类IoT数据收集。特别是,无人机根据收集的数据生成候选块,然后通过在基于无人机的区块链网络中的轻量级证明共识机制进行审核。为了激励有效的区块链,同时降低运营成本,在Active UAV上建造了一个利益池,同时鼓励其他无人机的利益分享的利益投资。该问题的制定是通过共同研究IoT传输,通过投资和利润共享的激励措施以及无人用的部署策略来通过单位时间来最大化整体利润。然后,将问题以分布的方式解决,同时将其分成两层。内层结合了物联网传输和激励设计,这些设计分别通过大型系统近似和一流的 - 阵营 - 追随者Stackelberg游戏分析来解决。无人机部署的外层采用多代理的深层确定性政策梯度方法进行。结果表明,与基准相比,我们提议的学习过程和无人机部署的融合,也证明了我们的提案的绩效优势。
The Internet of Things (IoT) can be conveniently deployed while empowering various applications, where the IoT nodes can form clusters to finish certain missions collectively. In this paper, we propose to employ unmanned aerial vehicles (UAVs) to assist the clustered IoT data collection with blockchain-based security provisioning. In particular, the UAVs generate candidate blocks based on the collected data, which are then audited through a lightweight proof-of-stake consensus mechanism within the UAV-based blockchain network. To motivate efficient blockchain while reducing the operational cost, a stake pool is constructed at the active UAV while encouraging stake investment from other UAVs with profit sharing. The problem is formulated to maximize the overall profit through the blockchain system in unit time by jointly investigating the IoT transmission, incentives through investment and profit sharing, and UAV deployment strategies. Then, the problem is solved in a distributed manner while being decoupled into two layers. The inner layer incorporates IoT transmission and incentive design, which are tackled with large-system approximation and one-leader-multi-follower Stackelberg game analysis, respectively. The outer layer for UAV deployment is undertaken with a multi-agent deep deterministic policy gradient approach. Results show the convergence of the proposed learning process and the UAV deployment, and also demonstrated is the performance superiority of our proposal as compared with the baselines.