Developing and Deploying Sensor Network Applications with AnduIN - - PowerPoint PPT Presentation
Developing and Deploying Sensor Network Applications with AnduIN - - PowerPoint PPT Presentation
Developing and Deploying Sensor Network Applications with AnduIN Daniel Klan Katja Hose Kai-Uwe Sattler Databases & Information Systems Group Ilmenau University of Technology, Germany Outline Introduction AnduIN Query
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Outline
Introduction AnduIN Query Specification Query Optimization Case Study Conclusion
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Introduction
Anomalies in sensor networks
Sensors measuring the temperature
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Introduction
Anomalies in sensor networks
Detecting anomalies in sensor data streams
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Introduction
Anomalies in sensor networks
Determine anomaly regions
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Introduction
Wireless sensor networks
Continuous monitoring (environment, traffic, ...) Limited capacities (computing power, battery lifetime, ...)
Query processing
Centralized processing In-network query processing Hybrid processing
Centralized processing INQP
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Introduction
Problem:
Which parts of a query to evaluate within the network?
AnduIN:
Combining in-network query processing with data stream
processing
Query decomposition and optimization,
- bjective: minimization of energy consumption
Comfortable way of specifying queries
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AnduIN
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Logical Sensor Networks
Registering a sensor Registering a logical sensor network
CREATE STREAM net_stream (id int, temperatur double) NETWORK [ 15 (6, 9)] SAMPLING 30 SECONDS
15 6 9
ADD SENSOR 15 (temperature double, humidity double) LOCALIZATION [47° 25’, 010° 59’]
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Web-based Box-and-Arrow Frontend
Example query:
Detecting anomaly regions based on bursts
CREATE STREAM s_burst AS SELECT timestamp, temperature FROM net_stream [ burst-detection(w => 1000, threshold =>‘forecast‘) ]; SELECT timestamp, temperature FROM s_burst [anomaly-region (t => 0.5)]
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Web-based Box-and-Arrow Frontend
Example query:
Detecting anomaly regions based on bursts
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Query Decomposition and Optimization
logical plan
anomaly region detection burst detection sampling projection
source a b p s sink
possible physical query plans
a b p s a b p s a b p s a b p s
centralized in-network
transformation
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Cost Model
Costs for in-network query processing Factors influencing costs:
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Cost Model
Costs for in-network query processing No leader nodes
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Cost Model
Costs for in-network query processing Considering leader nodes
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Case Study
Detect anomaly regions based on bursts 100 nodes
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Summary
AnduIN
Declaritive query formulation Comprehensive optimization of complex queries Cost model
Future Work
Improving the implementation Multi-query optimization Online query optimization
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Thank you...
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Case Study
Tmote Sky sensor nodes with
16 bit MCU MSP430F1611, 4 MHz clock rate, IEEE 802.15.4 compatible CC2420 transceiver with 250kBit/s