Processing Continuous Range Queries with Spatiotemporal Tolerance

Continuous queries are often employed to monitor the locations of mobile objects (MOs), which are determined by sensing devices like GPS receivers. In this paper, we tackle two challenges in processing continuous range queries (CRQs): coping with data uncertainty inherently associated with location data, and reducing the energy consumption of battery-powered MOs. We propose the concept of spatiotemporal tolerance for CRQ to relax a query's accuracy requirements in terms of a maximal acceptable error. Unlike previous works, our definition considers tolerance in both the spatial and temporal dimensions, which offers applications more flexibility in specifying their individual accuracy requirements. As we will show, these tolerance bounds can provide well-defined query semantics in spite of different sources of data uncertainty. In addition, we present efficient algorithms that carefully control when an MO should sense or report a location, while satisfying these tolerances. Thereby, we particularly reduce the number of position sensing operations substantially, which constitute a considerable source of energy consumption. Extensive simulations confirm that the proposed algorithms result in large energy savings compared to nontolerant query processing.

[1]  Dieter Pfoser,et al.  Capturing the Uncertainty of Moving-Object Representations , 1999, SSD.

[2]  Pedro José Marrón,et al.  Mobility modeling of outdoor scenarios for MANETs , 2005, 38th Annual Simulation Symposium.

[3]  Kurt Rothermel,et al.  Architecture of a large-scale location service , 2002, Proceedings 22nd International Conference on Distributed Computing Systems.

[4]  Kien A. Hua,et al.  Real-time processing of range-monitoring queries in heterogeneous mobile databases , 2006, IEEE Transactions on Mobile Computing.

[5]  Andreas Haeberlen,et al.  Practical robust localization over large-scale 802.11 wireless networks , 2004, MobiCom '04.

[6]  Per K. Enge,et al.  Global positioning system: signals, measurements, and performance [Book Review] , 2002, IEEE Aerospace and Electronic Systems Magazine.

[7]  Mani B. Srivastava,et al.  Emerging techniques for long lived wireless sensor networks , 2006, IEEE Communications Magazine.

[8]  Roger Zimmermann,et al.  Distributed Continuous Range Query Processing on Moving Objects , 2006, DEXA.

[9]  Donald F. Towsley,et al.  Packet audio playout delay adjustment: performance bounds and algorithms , 1998, Multimedia Systems.

[10]  Kyriakos Mouratidis,et al.  A threshold-based algorithm for continuous monitoring of k nearest neighbors , 2005, IEEE Transactions on Knowledge and Data Engineering.

[11]  Ling Liu,et al.  MobiEyes: A Distributed Location Monitoring Service Using Moving Location Queries , 2006, IEEE Transactions on Mobile Computing.

[12]  Scott Shenker,et al.  Supporting real-time applications in an Integrated Services Packet Network: architecture and mechanism , 1992, SIGCOMM '92.

[13]  Sunil Prabhakar,et al.  Adaptive Stream Filters for Entity-based Queries with Non-Value Tolerance , 2005, VLDB.

[14]  Yannis Kotidis,et al.  Snapshot queries: towards data-centric sensor networks , 2005, 21st International Conference on Data Engineering (ICDE'05).

[15]  Gregory D. Abowd,et al.  Towards a Better Understanding of Context and Context-Awareness , 1999, HUC.

[16]  Walid G. Aref,et al.  Query Indexing and Velocity Constrained Indexing: Scalable Techniques for Continuous Queries on Moving Objects , 2002, IEEE Trans. Computers.

[17]  Hanan Samet,et al.  Spatial Data Structures , 1995, Modern Database Systems.

[18]  Wei Hong,et al.  The design of an acquisitional query processor for sensor networks , 2003, SIGMOD '03.

[19]  J. Rankin,et al.  An error model for sensor simulation GPS and differential GPS , 1994, Proceedings of 1994 IEEE Position, Location and Navigation Symposium - PLANS'94.

[20]  Edward Y. Chang,et al.  Adaptive stream resource management using Kalman Filters , 2004, SIGMOD '04.

[21]  Christopher Olston,et al.  Distributed top-k monitoring , 2003, SIGMOD '03.

[22]  Upkar Varshney,et al.  Location management for mobile commerce applications in wireless Internet environment , 2003, TOIT.

[23]  Kurt Rothermel,et al.  Energy-efficient Tracking of Mobile Objects with Early Distance-based Reporting , 2007, 2007 Fourth Annual International Conference on Mobile and Ubiquitous Systems: Networking & Services (MobiQuitous).

[24]  Reynold Cheng,et al.  Energy-Efficient Monitoring of Mobile Objects with Uncertainty-Aware Tolerances , 2007, 11th International Database Engineering and Applications Symposium (IDEAS 2007).

[25]  A. Prasad Sistla,et al.  Updating and Querying Databases that Track Mobile Units , 1999, Distributed and Parallel Databases.

[26]  Christian Bettstetter,et al.  Mobility modeling in wireless networks: categorization, smooth movement, and border effects , 2001, MOCO.

[27]  Jennifer Widom,et al.  Adaptive filters for continuous queries over distributed data streams , 2003, SIGMOD '03.

[28]  Paul Lukowicz,et al.  Power and accuracy trade-offs in sound-based context recognition systems , 2007, Pervasive Mob. Comput..

[29]  Jianliang Xu,et al.  A generic framework for monitoring continuous spatial queries over moving objects , 2005, SIGMOD '05.