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Sensors 2017, 17(6), 1427;

Spatial Indexing for Data Searching in Mobile Sensing Environments

Institute for Communication Systems (ICS), University of Surrey, Guildford GU2 7XH, UK
Department of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, Ren’ai Road Dushu Lake Higher Education Town SIP, Suzhou 215123, China
Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, VIC 3010, Australia
Author to whom correspondence should be addressed.
Received: 28 February 2017 / Revised: 4 June 2017 / Accepted: 14 June 2017 / Published: 18 June 2017
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Data searching and retrieval is one of the fundamental functionalities in many Web of Things applications, which need to collect, process and analyze huge amounts of sensor stream data. The problem in fact has been well studied for data generated by sensors that are installed at fixed locations; however, challenges emerge along with the popularity of opportunistic sensing applications in which mobile sensors keep reporting observation and measurement data at variable intervals and changing geographical locations. To address these challenges, we develop the Geohash-Grid Tree, a spatial indexing technique specially designed for searching data integrated from heterogeneous sources in a mobile sensing environment. Results of the experiments on a real-world dataset collected from the SmartSantander smart city testbed show that the index structure allows efficient search based on spatial distance, range and time windows in a large time series database. View Full-Text
Keywords: mobile sensor data search; opportunistic sensing; mobile sensing; spatial indexing; Web of Things (WoT) mobile sensor data search; opportunistic sensing; mobile sensing; spatial indexing; Web of Things (WoT)

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Zhou, Y.; De, S.; Wang, W.; Moessner, K.; Palaniswami, M.S. Spatial Indexing for Data Searching in Mobile Sensing Environments. Sensors 2017, 17, 1427.

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