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.)
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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.
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Motivation: Real-Time Geo-Centric Sensor Network Applications
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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.
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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]
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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.
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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
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STDCS Components: Local Virtual Address Assignment
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STDCS Components: Spatio-Temporal Data Indexing
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STDCS Components: Reading Delivery and Querying
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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.
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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
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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.
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Experimental Results: STDCS vs. Query Hotspots
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Experimental Results: STDCS vs. Query Hotspots
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Experimental Results: Switching Time Effect
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Experimental Results: Switching Time vs. Node Deaths
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Experimental Results: Adaptive Hotspot Decomposition
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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.
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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/
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Thank You
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