A Distributed Storage and Computation k-Nearest Neighbor Algorithm Based Cloud-Edge Computing for Cyber-Physical-Social Systems

• Published in: IEEE access Vol. 8; pp. 50118 - 50130
• Main Authors: Zhang, Wei, Chen, Xiaohui, Liu, Yueqi, Xi, Qian
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Big Data; Boundaries; Cloud computing; cloud-edge computing; Clustering algorithms; CPSS; Datasets; Distributed databases; distributed storage and computation; Edge computing; Error analysis; k-nearest neighbor boundary; K-nearest neighbors algorithm; kNN; Machine learning; Machine learning algorithms; Massive data points; Mathematical analysis; Partitioning algorithms
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2974764

The k-nearest neighbor (kNN) algorithm is a classic supervised machine learning algorithm. It is widely used in cyber-physical-social systems (CPSS) to analyze and mine data. However, in practical CPSS applications, the standard linear kNN algorithm struggles to efficiently process massive data sets. This paper proposes a distributed storage and computation k-nearest neighbor (D-kNN) algorithm. The D-kNN algorithm has the following advantages: First, the concept of k-nearest neighbor boundaries is proposed and the k-nearest neighbor search within the k-nearest neighbors boundaries can effectively reduce the time complexity of kNN. Second, based on the k-neighbor boundary, massive data sets beyond the main storage space are stored on distributed storage nodes. Third, the algorithm performs k-nearest neighbor searching efficiently by performing distributed calculations at each storage node. Finally, a series of experiments were performed to verify the effectiveness of the D-kNN algorithm. The experimental results show that the D-kNN algorithm based on distributed storage and calculation effectively improves the operation efficiency of k-nearest neighbor search. The algorithm can be easily and flexibly deployed in a cloud-edge computing environment to process massive data sets in CPSS.