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  • 1. STDCS: A Spatio-Temporal Data-Centric Storage Scheme For Real-Time Sensornet Applications Mohamed Aly (University of Pittsburgh & Yahoo, Inc.) In collaboration with Anandha Gopalan (University of Pittsburgh, Imperial College) and Jerry Zhao, Adel Youssef (Google, Inc.)
  • 2. Motivation: Real-Time Geo-Centric Sensor Network Applications
    • Globally deployed sensor around the globe.
    • Clusters of sensors forming networks.
    • Mobile users roaming across the networks.
    • Real-time geo-centric ad-hoc queries issued from within or nearby the queried area.
    • The sensor network is responsible of answering these queries directly from the sensors rather than from base stations.
    • Examples:
      • Bronx Zoo cluster.
      • Disaster management cluster.
  • 3. Motivation: Real-Time Geo-Centric Sensor Network Applications
  • 4. Data Storage Options in Sensor Networks
    • Base Station Storage:
      • Events are sent to base stations where queries are issued and evaluated.
      • Best suited for continuous queries.
    • In-Network Storage (INS):
      • Events are stored in the sensor nodes.
      • Best suited for ad-hoc queries.
      • All previous INS schemes were Data-Centric Storage (DCS) schemes.
  • 5. In-Network Data-Centric Storage (DCS)
    • Mainly to answer range queries.
    • Quality of Data (QoD) of ad-hoc queries.
    • Assign a value-range of readings for each sensor.
    • Examples:
      • Distributed Hash Tables (DHT) [Shenker et. al., HotNets’02]
      • Geographic Hash Tables (GHT) [Ratnasamy et. al., WSNA’02]
      • Distributed Index for Multi-dimensional data (DIM) [Li et. al., SenSys’03, Aly et. al., DMSN’05, MOBIQUITOUS’06]
      • K-D Tree based Data-Centric Storage (KDDCS) [Aly et. al., CIKM’06]
  • 6. STDCS Overview
    • Motivation:
      • No previous INS schemes adopting geo-centric storage.
      • Expected techniques may be:
        • Local storage.
        • Spatial storage
    • Design Goal:
      • Load-Balancing of storage load among sensors
    • Differences from previous schemes:
      • Temporally evolving spatial indexing scheme to balance query load among sensors.
      • Dynamic query hotspot detection and decomposition.
  • 7. Roadmap
    • Motivation: Real-Time Geo-Centric applications.
    • Background: Data-Centric Storage (DCS).
    • Problem Statement: Real-Time Geo-Centric Storage.
    • Scheme Overview: STDCS.
    • STDCS Components
      • Local Virtual address assignment
      • Spatio-Temporal data indexing.
      • Point-to-point data delivery.
      • Query processing.
      • Adaptive hotspot decomposition.
    • Experimental Results
    • Conclusions
  • 8. STDCS Components: Local Virtual Address Assignment
  • 9. STDCS Components: Spatio-Temporal Data Indexing
  • 10. STDCS Components: Reading Delivery and Querying
  • 11. STDCS Components: Adaptive Hotspot Decomposition
    • Motivation:
      • Dynamic query hotspots as time progresses.
    • Observation:
      • Recurrent querying scenarios across the day, the week, etc.
    • Technique:
      • Continuously keeping track of hotspots using the Average Querying Frequency (AQF) metric.
      • Dynamically chaning the switching time to decompose hotspots.
  • 12. Roadmap
    • Motivation: Real-Time Geo-Centric applications.
    • Background: Data-Centric Storage (DCS).
    • Problem Statement: Real-Time Geo-Centric Storage.
    • Scheme Overview: STDCS.
    • STDCS Components
      • Local Virtual address assignment
      • Spatio-Temporal data indexing.
      • Point-to-point data delivery.
      • Query processing.
      • Adaptive hotspot decomposition.
    • Experimental Results
    • Conclusions
  • 13. Simulation Description
    • Compare: STDCS, local storage, spatial indexing.
    • A cluster of stationary sensors (with random locations).
    • Each sensor senses a reading each 10 min.
    • Sensor reading = 1 packet.
    • Sensor capacity = 20 readings (packets)
    • Multiple mobile users.
    • A query: random sensor, radius, and type.
    • Two phases: initialization (3 hours of readings) & running (1 day of readings and queries).
    • Metrics: throughput, energy level, node deaths.
  • 14. Experimental Results: STDCS vs. Query Hotspots
  • 15. Experimental Results: STDCS vs. Query Hotspots
  • 16. Experimental Results: Switching Time Effect
  • 17. Experimental Results: Switching Time vs. Node Deaths
  • 18. Experimental Results: Adaptive Hotspot Decomposition
  • 19. Conclusions
    • STDCS: A real-time geo-centric data storage scheme.
    • A new concept of spatio-temporal data indexing.
    • Ability to dynamically cope with dynamic loads and query hotspots.
  • 20. Acknowledgment
    • This work has been partly supported by:
      • Google, Inc.
      • The “Secure CITI: A Secure Critical Information Technology Infrastructure for Disaster Management (S-CITI)” project funded through the ITR Medium Award ANI-0325353 from the National Science Foundation (NSF). For more information, please visit:
        • http://www.cs.pitt.edu/s-citi/
  • 21. Thank You Questions ?