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An Approach to Recovery of Critical Data of Smart Cities Using Blockchain

机译:利用区块链恢复智慧城市关键数据的方法

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摘要

Smart cities are the next wave of rapid expansion of Internet of Things (IoT). A smart city is a designation given to a city that incorporates information and communication technologies (ICT) to enhance the quality and performance of urban services, such as energy, transportation, healthcare, communications, entertainments, education, e-commerce, businesses, city management, and utilities, to reduce resource consumption, wastage and overall costs. The overarching aim of a smart city is to enhance the quality of living for its residents and businesses, through technology. In a large ecosystem, like a smart city, many organizations and companies collaborate with the smart city government to improve the smart city. These entities may need to store and share critical data with each other. A smart city has several thousands of smart devices and sensors deployed across the city. Storing critical data in a secure and scalable manner is an important issue in a smart city. While current cloud-based services, like Splunk and ELK (Elasticsearch-Logstash-Kibana), offer a centralized view and control over the IT operations of these smart devices, it is still prone to insider attacks, data tampering, and rogue administrator problems. In this thesis, we present an approach using blockchain to recovering critical data from unauthorized modifications. We use extensive simulations based on complex adaptive system theory, for evaluation of our approach. Through mathematical proof we proved that the approach always detects an unauthorized modification of critical data.
机译:智慧城市是物联网(IoT)快速扩展的下一波浪潮。智慧城市是对具有信息和通信技术(ICT)的城市的称号,以提高城市服务的质量和性能,例如能源,交通,医疗,通信,娱乐,教育,电子商务,商业,城市管理和公用事业,以减少资源消耗,浪费和总成本。智慧城市的总体目标是通过技术提高居民和企业的生活质量。在像智慧城市这样的大型生态系统中,许多组织和公司与智慧城市政府合作以改善智慧城市。这些实体可能需要相互存储和共享关键数据。智慧城市在整个城市中部署了数千个智能设备和传感器。以安全和可扩展的方式存储关键数据是智慧城市中的重要问题。虽然当前的基于云的服务(例如Splunk和ELK,提供对这些智能设备的IT操作的集中式视图和控制),但仍然容易受到内部人员攻击,数据篡改和流氓管理员的困扰。在本文中,我们提出了一种使用区块链从未经授权的修改中恢复关键数据的方法。我们使用基于复杂自适应系统理论的广泛仿真来评估我们的方法。通过数学证明,我们证明了该方法始终可以检测到关键数据的未经授权的修改。

著录项

  • 作者

    Mishra, Vineet.;

  • 作者单位

    Arizona State University.;

  • 授予单位 Arizona State University.;
  • 学科 Computer science.
  • 学位 M.S.
  • 年度 2017
  • 页码 61 p.
  • 总页数 61
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:38:58

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