The Cougar Approach to InNetwork Query Processing in Sensor Networks

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The Cougar Approach to In-Network Query
Processing in Sensor Networks

• Authors Yong Yao and Johannes Gehrke
• Cornell University
• Presented by Nanyan Jiang

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Talk Outline

• Motivation
• Declarative queries
• In-network processing
• Architecture
• Research Problems
• Aggregation
• Query plan and optimization
• Catalog management
• Multi-query optimization
• Conclusion and Discussion

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Motivation

• Many Sensor Network Applications are Data
  oriented
• Queries natural and efficient data processing
  mechanism
• Easy, e.g. declarative query
• Enable optimizations through abstraction
• Aggregates common case
• In-network processing a must
• Sensor networks power and bandwidth constrained
• Communication dominates power cost

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Cougar Approach Distributed Sensor Databases

• Access to sensors is dictated by the query
  workload
• Trade-off between local computation on devices
  and reduced communication (in-network processing)

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The Cougar View of Sensor Networks

• Traditional
• Procedural addressing of individual sensor nodes
  user specifies how task executes, data is
  processed centrally.
• Cougar
• Complex declarative querying and tasking. User
  isolated from how the network works, in-network
  distributed processing.

Queries
Temperature
Time Value
200 15 400 12
Temperature
Time Value
200 10 400 13
Pressure
Humidity
Temperature
Time Value
Time Value
Time Value
100 30 400 35
230 70 330 75
200 20 300 18
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Database Approaches for accessing Sensor Networks

• Warehousing approach
• Device data is extracted in a predefined way
• Device data is stored in a centralized DB server
• Queries are evaluated on the centralized DB
  server
• Distributed approach
• Queries are evaluated by contacting devices
• Portions of queries are executed on the devices

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Query examples

• Snapshot queries
• How many empty bird nests are in the northeastern
• quadrant of the forest?
• SELECT SUM(s)
• FROM SensorData s
• WHERE s.nest empty and s.loc in
  (50,50,100,100)
• Long-running queries
• Notify me over the next hour whenever the number
  of
• empty nests in an area exceeds a threshold.
• SELECT s.area, SUM(s)
• FROM SensorData s
• WHERE s.nest empty
• GROUP BY s.area
• HAVING SUM(s) T
• DURATION (now, now60)
• EVERY 5

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Cougar System Architecture
Higher-level tasking and analysis
Proxy Server
Frontend
Diffusion Routing
Query Proxy
Node
Diffusion Routing
Signal Processing
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Cougar (Mica Mote)

• A platform for testing query processing
  techniques over ad-hoc sensor networks
• Three tier system
• Running TinyOS, an embedded operating system from
  Berkeley, on the motes
• Server handling query interface with motes and
  database mapping
• GUI human usable interface

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Three Tier Architecture
Thin Client GUI
Wire/Wireless Communication
Server (Java)
Radio Communication
TinyOS/NesC
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Architecture

• The gateway node
• A query optimizer generates distributed query
  plans
• Query plan is created according to catalog
  information and the query specification
• Data flow (between sensors)
• Computation plan (at each sensor)
• Plan is disseminated to the network
• Sensor node
• Execute plan accordingly

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Query Plan

• Example What is the quietest classroom in Upson
  Hall?
• A flow block is the basic component of a query
  plan
• Determines the data flow between sensors
• Determines the computation plan.

• Query plan
• computation
• compute average acoustic value of each open
  classroom
• Select the room with the smallest number
• The output of first level aggregation is the
  input of 2nd level
• communication
• structure, e.g. Tree or DAG

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How To Execute The Query?

• Sensor nodes have computation and storage
  capabilities. Two choices Centralized processing
  or in-network processing. Why in-network
  processing?
• Sensor networks are power constrained.
• Local computation is much cheaper than
  communication.
• A lot of sensor data but few queries, and only a
  subset of data is involved in queries
• In-network processing provides a trade-off
  between computation and communication. Cougar
  prolongs the lifetime of sensor networks through
  distributed in-network processing and carefully
  designed sensor coordination

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Query Execution

• Server creates an optimized plan according to the
  user query, and catalog information.
• Query plan is forwarded to relevant sensor nodes.
  
• Sensor nodes coordinate to execute the query
  according to the specification of the query plan.
  
• Server gets results from gateway nodes.

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Research Issues

• In-network processing
• Aggregation
• Catalog Management
• Multi-query Optimization

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Aggregation
SELECT SUM(s) FROM SensorData s WHERE s.nest
empty EVERY 60 min

• Aggregation refers to delivering data from
  distributed source sensors to a central node for
  computation.
• Data records are delivered from source sensor
  nodes to designated leader node
• Aggregation is executed at the leader node

• Centralized Processing
• In-network Processing
• Gehrke et al. (Cougar)
• Madden et al (TAG)
• Forming the routing tree
• Compute an aggregate
• Chalermek et al. (Directed diffusion)

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In-network Processing
SELECT SUM(s) FROM SensorData s WHERE s.nest
empty EVERY 60 min

• With in-network processing,
• the number of results at each
• edge remains constant.
• Reduces communication overhead
• Reduces energy consumption
• Increases network lifetime

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Schemes of In-network processing
View Node
Query
Result
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Approaches of In-network Processing
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Catalog Management

• To generate plans for queries, optimizer needs
  metadata about the status of sensor network to
  evaluate costs and benefits of different plans
• Catalog could be built/maintained on server to
  hold information like sensor position, density,
  connectivity, system workload, and network
  stability
• Queries could be used to update the catalog
  periodically
• Or catalogs may be assembled dynamically through
  gossip-style information dissemination
• Due to size of metadata and dynamics of sensor
  networks, it is likely prohibitive to collect all
  metadata at central node and to keep them
  up-to-date.
• Need to define synopsis data structures that are
  cheap to create and maintain, but still contain
  enough detail for query optimization

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Multi Query Optimization

• Multi-query optimization can achieve further
  energy savings
• identify common sub-aggregates shared among
  queries
• consider query and sensor update probabilities
• return results of updated queries ?
• return values of updated sensors ?
• determine suitable routes for sending back the
  results

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Conclusion

• View sensor networks as database
• Declarative queries
• In-network query processing

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Discussion

• Decentralized query execution/optimization
• A single site cannot maintain precise
  meta-information about the complete system
• Adaptive query processing
• Conditions in a sensor network change over time
• sensors fail, move or disconnect

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Thank You