ST-Trie: A Novel Indexing Scheme for Efficiently Querying Heterogeneous, Spatiotemporal IoT Data

Hawon Chu, Jaeseong Kim, Seounghyeon Kim, Young-Kyoon Suh, Ryong Lee, Rae-Young Jang and Minwoo Park
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Hawon Chu: School of Computer Science and Engineering, Kyungpook National University, Daegu 41566, Korea
Jaeseong Kim: School of Computer Science and Engineering, Kyungpook National University, Daegu 41566, Korea
Seounghyeon Kim: School of Computer Science and Engineering, Kyungpook National University, Daegu 41566, Korea
Young-Kyoon Suh: School of Computer Science and Engineering, Kyungpook National University, Daegu 41566, Korea
Ryong Lee: Research Data Sharing Center, Korea Institute of Science and Technology Information, Daejeon 34141, Korea
Rae-Young Jang: Research Data Sharing Center, Korea Institute of Science and Technology Information, Daejeon 34141, Korea
Minwoo Park: Research Data Sharing Center, Korea Institute of Science and Technology Information, Daejeon 34141, Korea

Sustainability, 2020, vol. 12, issue 22, 1-21

Abstract: Recently, various environmental data, such as microdust pollution, temperature, humidity, etc., have been continuously collected by widely deployed Internet of Things (IoT) sensors. Although these data can provide great insight into developing sustainable application services, it is challenging to rapidly retrieve such data, due to their multidimensional properties and huge growth in volume over time. Existing indexing methods for efficiently locating those data expose several problems, such as high administrative cost, spatial overhead, and slow retrieval performance. To mitigate these problems, we propose a novel indexing scheme termed ST-Trie, for efficient retrieval over spatiotemporal IoT environment data. Given IoT sensor data with latitude, longitude, and time, the proposed scheme first converts the three-dimensional attributes to one-dimensional index keys. The scheme then builds a trie-based index, consisting of internal nodes inserted by the converted keys and leaf nodes containing the keys and pointers to actual IoT data. We leverage this index to process various types of queries. In our experiments with three real-world datasets, we show that the proposed ST-Trie index outperforms existing approaches by a substantial margin regarding response time. Furthermore, we show that the query processing performance via ST-Trie also scales very well with an increasing time interval. Finally, we demonstrate that when compressed, the ST-Trie index can significantly reduce its space overhead by approximately a factor of seven.

Keywords: Internet of Things sensors; environment data; spatiotemporal indexing; trie; query processing (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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