4.5 Article

SDN-based cross-domain cooperative method for trusted nodes recommendation in Mobile crowd sensing

期刊

PEER-TO-PEER NETWORKING AND APPLICATIONS
卷 14, 期 6, 页码 3793-3805

出版社

SPRINGER
DOI: 10.1007/s12083-021-01217-z

关键词

Mobile crowd sensing; Trusted nodes recommendation; SDN; Cross-domain collaborative filtering

资金

  1. National Science and Technology Major Project [2016ZX03001023-005]
  2. National Natural Science Foundation of China [61403109]
  3. China Postdoctoral Science Foundation [2019 M651263]
  4. Heilongjiang Natural Science Foundation [LH2020F034]
  5. Scientific Research Fund of Heilongjiang Provincial Education Department [12541169]

向作者/读者索取更多资源

This study presents a trusted sensing node recommendation method based on SDN, which utilizes cross-domain collaborative filtering to identify similar credible sensing nodes and achieves better recommendation accuracy with shorter time consumption.
Aiming at the problem of unreliable data quality caused by sensing node uncertainty in mobile crowd sensing, a cross-domain collaborative filtering trusted sensing node recommendation method based on SDN is proposed. Firstly, SDN is introduced to decouple the service surface and the control surface, and it is convenient to manage sensing nodes and reduce the burden of server for task allocation. Then, through cross-domain collaborative filtering method, find sensing nodes which show similar credibility in the historical task allocation and complete some similar tasks with target sensing nodes. Finally, the recommendation value of the sensing node in the target task is obtained though the current ability of sensing nodes, and their distance from target tasks, and similar sensing nodes' credibility in the target task and time decay, at last, the trusted sensing node is selected. Simulation experiments verify that when selecting a trusted sensing node, the method proposed in this paper has better recommendation accuracy, and the time is shorter. In addition, it also proves that when the sensing data of the same data quality is obtained, the incentive cost is lower.

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