Research Output

KNN-Based Approximate Outlier Detection Algorithm Over IoT Streaming Data

  KNN-Based outlier detection over IoT streaming data is a fundamental problem, which has many applications. However, due to its computational complexity, existing efforts cannot efficiently work in the IoT streaming data. In this paper, we propose a novel framework named GAAOD(Grid-based Approximate Average Outlier Detection) to support KNN-Based outlier detection over IoT streaming data. Firstly, GAAOD introduces a grid-based index to manage summary information of streaming data. It can self-adaptively adjust the resolution of cells, and achieve the goal of efficiently filtering objects that almost cannot become outliers. Secondly, GAAOD uses a min-heap-based algorithm to compute the distance upper-/lower-bound between objects and their k-th nearest neighbors respectively. Thirdly, GAAOD utilizes a k-skyband based algorithm to maintain outliers and candidate outliers. Theoretical analysis and experimental results verify the efficiency and accuracy of GAAOD. INDEX TERMS IoT streaming data, KNN-based outliers, indexes, error guarantee.

  • Type:


  • Date:

    28 February 2020

  • Publication Status:


  • Publisher

    Institute of Electrical and Electronics Engineers (IEEE)

  • DOI:


  • Funders:

    Liaoning Provincial Department of Education Science Foundation; Young and Middle-Aged Science and Technology Innovation Talent Support Plan of Shenyang; Natural Science Foundation of Liaoning Province; National Natural Science Foundation of China


Zhu, R., Ji, X., Yu, D., Tan, Z., Zhao, L., Li, J., & Xia, X. (2020). KNN-Based Approximate Outlier Detection Algorithm Over IoT Streaming Data. IEEE Access, 8, 42749-42759.



Anomaly detection, Microsoft Windows, Indexes, Monitoring, Approximation algorithms, Sensors, Heuristic algorithms

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