Mobiiscape: Middleware support for scalable mobility pattern monitoring of moving objects in a large-scale city

  • Authors:

  • Byoungjip Kim;Sangjeong Lee;Youngki Lee;Inseok Hwang;Yunseok Rhee;Junehwa Song

  • Affiliations:

  • Computer Science, Korea Advanced Institute of Science and Technology (KAIST), South Korea;Computer Science, Korea Advanced Institute of Science and Technology (KAIST), South Korea;Computer Science, Korea Advanced Institute of Science and Technology (KAIST), South Korea;Computer Science, Korea Advanced Institute of Science and Technology (KAIST), South Korea;Digital Information Engineering, Hankuk University of Foreign Studies, South Korea;Computer Science, Korea Advanced Institute of Science and Technology (KAIST), South Korea

  • Venue:
  • Journal of Systems and Software
  • Year:
  • 2011

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Abstract

With the explosive proliferation of mobile devices such as smartphones, tablets, and sensor nodes, location-based services are getting even more attention than before, considered as one of the killer applications in the upcoming mobile computing era. Developing location-based services necessarily requires an effective and scalable location data processing technology. In this paper, we present Mobiiscape, a novel location monitoring system that collectively monitors mobility patterns of a large number of moving objects in a large-scale city to support city-wide mobility-aware applications. Mobiiscape provides an SQL-like query language named Moving Object Monitoring Query Language (MQL) that allows applications to intuitively specify Mobility Pattern Monitoring Queries (MPQs). Further, Mobiiscape provides a set of scalable location monitoring techniques to efficiently process a large number of MPQs over a large number of location streams. The scalable processing techniques include a (1) Place Border Index, a spatial index for quickly searching for relevant queries upon receiving location streams, (2) Place-Based Window, a spatial-purpose window for efficiently detecting primitive mobility patterns, (3) Shared NFA, a shared query processing technique for efficiently matching complex mobility patterns, and (4) Attribute Pre-matching Bitmap, an in-memory data structure for efficiently filtering out moving objects based on their attributes. We have implemented a Mobiiscape prototype system. Then, we show the usefulness of the system by implementing promising location-based applications based on it such as a ubiquitous taxicab service and a location-based advertising. Also, we demonstrate the performance benefit of the system through extensive evaluation and comparison.