Title: A parallel configurable collection method for multi-source heterogeneous data in the internet of things based on spatiotemporal clustering

Authors: Yanrong Wang; Xiaokun Huang; Ying Li

Addresses: Continuous Education College, Jiaozuo University, Jiaozuo, Henan, China ' Sungkyunkwan University, Seoul, South Korea ' School of Information Engineering, Jiaozuo University, Jiaozuo, Henan, China

Abstract: To overcome the problems of delayed and poor robustness in traditional methods for multi-source heterogeneous data collection, this paper proposes a parallel configurable data collection method for IoT multi-source heterogeneous data based on spatiotemporal clustering. Firstly, design the overall plan for multi-source heterogeneous data collection in the Internet of Things. Then, insert spatiotemporal data and determine the encoding length, and use clustering methods to perform non-uniform rectangular encoding and mapping on the data, achieving adaptive GeoHash encoding for heterogeneous data. Construct a one-dimensional R-Tree to construct a temporal index. Finally, parallel configuration of multi-source heterogeneous data is carried out based on the index, and data parallel configuration collection is ultimately achieved. The results show that the data collection time of our method is only 2.3 seconds, and the recall rate can reach 96.9%, indicating that our method can improve data collection efficiency and robustness.

Keywords: spatiotemporal clustering; internet of things; multi-source heterogeneous data; time index; configurable; adaptive GeoHash encoding.

DOI: 10.1504/IJIPT.2024.146226

International Journal of Internet Protocol Technology, 2024 Vol.17 No.3, pp.111 - 122

Received: 19 Sep 2024
Accepted: 25 Nov 2024
Published online: 13 May 2025 *

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