Wireless Sensor Networks
In-Network Relational Databases
Jocelyn Botello
Overview
• Introduction
• Sensor Database System
• Projects
– TinyDB
– Cougar
• Maximum Performance
– Efficiency
– Optimization
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Introduction
• Minimization Goal:
– Network Traffic
– Amount of Transmitted Data
• Maximization Goal:
– Computing Capacity
– Power
• Acquire Data for Unlimited Time
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Sensor Database System
• Access data with no previous
knowledge
• Three-Layer Reference Model
• Relational Model
– Sensor Data: Time Series
– Stored Data: Relations
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TinyDB from Berkley
• Query Processor
• Multiple Query Concurrency
• Tree Routing
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TinyDB from Berkley
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Event- Based Queries
Actuation Queries
Lifetime- Based Queries
Monitoring Queries
Network Health Queries
Exploratory Queries
Aggregation Queries
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Cougar from Cornell
• Sensors
– Abstract Data Type Functions
– In-Network Processing
– Gateway Node
• Query Proxy
– Small Database Component
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Efficiency
• Communication Failure
• Reliable Data
– Uncertainty of Data
– Security of Data
• Network’s Power Life
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Communication Failure
• Sensors Physically Dependable
– Outside Factors
• Keep Data Alive
– Back-Up
– Accessibility, Availability
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Reliable Data: Uncertainty
• Level of Accuracy Vs Cost of
Computation
• Desired Accuracy
• Probabilistic Threshold Query
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Reliable Data: Security
• Network Specific
– Level of Security
– Access Points/Rights
• Affects of Aggregation
• Dynamic
• Level of Security Vs Access Time
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Optimization
• Data Space Management
• Queries
• Aggregation
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Data Space Management
• Storage Nodes
– Minimize Traffic & Retrieve Time
• Switch Roles
– Busy Region
– Power Life
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Queries
• Independent, Dynamic
• Irrelevant Factors
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Power Management
Time Synchronization
Data Processing
Data Collection
• Maintaining Power Life
• Multiple, Nested Queries
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Aggregation
• Partial/Total Aggregation
• Selective Data
• Spatial Aggregation
– Spatial Moving Average
– Voroni Diagram
– Triangular Irregular Network
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Conclusion
• Maximum Performance
– Efficiency
• Reliable Data Vs Communication Failure
– Optimization
• Queries
• Aggregation
– Minimize Network Traffic
– Conservation of Power
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Future Work
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Power Management
Data Management
Data Collection
Data Processing
– Query Processing
• Network Design
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References
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[2] Y. Yao and J. Gehrke, “The cougar approach to innetwork query processing in sensor networks,” ACM
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[3] Q. Luo and H. Wu, “System design issues in sensor
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[4] Zechinelli-Martini, Jose-Luis, and I. Elias-Morales,
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[10] S. M. Michael J. Franklin, Joseph M. Hellerstein,
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[11] Q. Ren and Q. Liang, “Query processing optimization
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[12] Q. Ren and Q. Lian, “A quality-guaranteed and
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[13] L. Q. Zhuang, J. B. Zhang, D. H. Zhang, and Y. Z.
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[14] G. K. J. B. Jeffrey Considine, Feifei Li, “Approximate
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[15] P. Flajolet and G. N. Martin, “Probablistic counting
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[16] M. Sharifzadeh and C. Shababi, “Supporting spatial
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