Spatial Indexing for Data Searching in Mobile Sensing Environments
AuthorZhou, Y; De, S; Wang, W; Moessner, K; Palaniswami, MS
University of Melbourne Author/sPalaniswami, Marimuthu
AffiliationElectrical and Electronic Engineering
Document TypeJournal Article
CitationsZhou, Y., De, S., Wang, W., Moessner, K. & Palaniswami, M. S. (2017). Spatial Indexing for Data Searching in Mobile Sensing Environments. SENSORS, 17 (6), https://doi.org/10.3390/s17061427.
Access StatusOpen Access
Open Access at PMChttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC5492522
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.
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