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    Spatial Indexing for Data Searching in Mobile Sensing Environments

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    7
    Author
    Zhou, Y; De, S; Wang, W; Moessner, K; Palaniswami, MS
    Date
    2017-06-01
    Source Title
    Sensors
    Publisher
    MDPI
    University of Melbourne Author/s
    Palaniswami, Marimuthu
    Affiliation
    Electrical and Electronic Engineering
    Metadata
    Show full item record
    Document Type
    Journal Article
    Citations
    Zhou, 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 Status
    Open Access
    URI
    http://hdl.handle.net/11343/259624
    DOI
    10.3390/s17061427
    Open Access at PMC
    http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5492522
    Abstract
    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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