KNN-Based Approximate Outlier Detection Algorithm Over IoT Streaming Data

Abstract

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.