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LDV: A Lightweight DAG-Based Blockchain for Vehicular Social Networks

机译:LDV:用于车辆社交网络的基于轻量级的笨蛋区块链条

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摘要

As social networks are integrated into the Vehicular Ad Hoc Networks (VANETs), the emerging Vehicular Social Networks (VSNs) have gained massive interests. However, the security and privacy of data generated by various applications in VSNs is a great challenge, which blocks the further development of VSNs. The emerging blockchain technology seems to be a good catalyst for the development of VSN with its high security and irreversible features, which can be also a data management tool for rapidly generated data of VSNs with tamper proof. However, the full duplicates of blockchain data need to be stored in each node to ensure security, which is unacceptable for vehicles with limited resource. In this paper, to address the above storage challenge, a lightweight Directed Acyclic Graph (DAG) based blockchain (LDV) is proposed for resource-constrained VSNs. Specifically, based on the in-depth analysis of VSNs, we propose the social-based data reduction approach. In detail, each node only stores the interested data within the topic groups of interest and ignores the irrelevant data. To avoid the huge storage cost within large-scale groups with large amounts of data, we further present the historical data pruning method within a group, which meets the storage requirement by reducing the number of duplicates stored in each node. Experimental results show that LDV can save 97.13% storage space and has good scalability.
机译:随着社交网络融入的<斜体XMLNS:mml =“http://www.w3.org/1998/math/mathml”xmlns:xlink =“http://www.w3.org/1999/xlink”>车辆ad hoc网络 (vanets),新兴车辆社交网络 (vsns)获得了大量利益。但是,VSN中各种应用程序生成的数据的安全性和隐私是一个很大的挑战,它阻止了VSN的进一步发展。新兴区块链技术似乎是具有其高安全性和不可逆功能的VSN的良好催化剂,这也可以是具有篡改证明的VSN的快速生成VSN数据的数据管理工具。然而,需要将区块链数据的全重复存储在每个节点中以确保安全性,这对于具有有限资源的车辆是不可接受的。在本文中,为了解决上述储存挑战,轻量级<斜体XMLNS:MML =“http://www.w3.org/1998/math/mathml”xmlns:xlink =“http://www.w3.org/1999/xlink”>定向非循环图 (DAG)基于区块链(LDV)被提出用于资源受限的VSN。具体地,基于对VSN的深入分析,我们提出了基于社会的数据还原方法。详细地,每个节点仅在关注的主题组中存储感兴趣的数据,并忽略无关数据。为避免具有大量数据的大型群体内的巨大存储成本,我们进一步介绍了一个组内的历史数据修剪方法,通过减少存储在每个节点中的重复次数来满足存储要求。实验结果表明,LDV可以节省97.13%的存储空间并具有良好的可扩展性。

著录项

  • 来源
    《IEEE Transactions on Vehicular Technology》 |2020年第6期|5749-5759|共11页
  • 作者单位

    National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Laboratory and the Cluster and Grid Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China;

    National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Laboratory and the Cluster and Grid Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China;

    National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Laboratory and the Cluster and Grid Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China;

    National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Laboratory and the Cluster and Grid Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Blockchain; Social networking (online); Memory; Bitcoin; Vehicles; Throughput;

    机译:区块链;社交网络(在线);记忆;比特币;车辆;吞吐量;

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