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相关概念视频

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RCoD:基于声誉的背景数据融合,用于移动物联网.

Samia Tasnim1, Niki Pissinou2, S Sitharama Iyengar2

  • 1Department of Electrical Engineering and Computer Science, The University of Toledo, Toledo, OH 43606, USA.

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概括

本研究引入了一种新的基于声誉的上下文感知数据融合 (RCoD) 机制,以提高移动物联网 (IoT) 系统中的数据准确性. RCoD有效地识别恶意参与者并恢复损坏的数据,确保可靠的传感器信息.

关键词:
对空气质量进行监测.语境 语境 语境 语境 语境数据分析数据分析.数据融合数据融合数据可靠性的数据可靠性.物联网的东西互联网.机器学习是机器学习.声誉 声誉 声誉 声誉在信任信任信任信任信任信任.

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科学领域:

  • 计算机科学 计算机科学
  • 数据科学数据科学数据科学
  • 传感器网络 传感器网络

背景情况:

  • 移动传感技术和物联网 (IoT) 正在迅速扩展.
  • 由于固有的漏洞,确保移动物联网系统中的数据可靠性和准确性是具有挑战性的.
  • 移动物联网中以人为中心的架构可能会导致不准确或损坏的数据,特别是恶意参与者.

研究的目的:

  • 为应对移动物联网系统中不准确数据预测的挑战.
  • 开发一种能够抵御恶意数据注入和腐败攻击的机制.
  • 从相关的数据流中恢复缺失或不准确的数据值.

主要方法:

  • 提出一个基于声誉的背景感知数据融合 (RCoD) 机制.
  • 使用上下文隐藏马尔科夫模型进行准确的实时数据预测.
  • 评估RCoD对开启和数据腐败攻击的弹性.

主要成果:

  • 即使大多数是恶意的,RCoD也准确地识别了诚实的参与者.
  • 该机制证明了对各种恶意数据注入率的弹性.
  • 使用北京空气质量数据集的实证评估证实了RCoD在最先进的方法上的优势.

结论:

  • 拟议的RCoD机制显著提高了移动物联网环境中的数据准确性和可靠性.
  • 在敌对参与者的存在下,RCoD为数据融合和预测提供了一个强大的解决方案.
  • 与声誉系统相结合的上下文感知数据融合对于安全可靠的物联网数据是有效的.