Improving Data Credibility for Mobile Crowdsensing with Clustering and Logical Reasoning

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• 关键词：Mobile crowdsensing ; Data credibility ; Clustering algorithm ; Logical reasoning
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：10040
• 期：1
• 页码：138-150
• 全文大小：811 KB
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• 作者单位：Tongqing Zhou (17)
  Zhiping Cai (17)
  Yueyue Chen (17)
  Ming Xu (17)
  
  17. College of Computer, National University of Defense Technology, Changsha, China
  
• 丛书名：Cloud Computing and Security
• ISBN：978-3-319-48674-1
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：10040

文摘

Mobile crowdsensing is a new paradigm that tries to collect a vast amount of data with the rich set of sensors on pervasive mobile devices. However, the unpredictable intention and various capabilities of device owners expose the application to potential dishonest and malicious contributions, bringing forth the important issues of data credibility assurance. Existed works generally attempt to increase data confidence level with the guide of reputation, which is very likely to be unavailable in reality. In this work, we propose CLOR, a general scheme to ensure data credibility for typical mobile crowdsensing application without requiring reputation knowledge. By integrating data clustering with logical reasoning, CLOR is able to formally separate false and normal data, make credibility assessment, and filter out the false ingredient. Simulation results show that improved data credibility can be achieved effectively with our scheme.