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Skyline Queries Against Mobile Lightweight Devices in MANETs

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M1 to M4 hold different hotel relations. M2 is interested in cheap and good hotels within the circle area. M1. M2. M3. M4. Outline ... – PowerPoint PPT presentation

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Title: Skyline Queries Against Mobile Lightweight Devices in MANETs


1
Skyline Queries Against Mobile Lightweight
Devices in MANETs
  • Zhiyong Huang1
  • Christian S. Jensen2
  • Hua Lu1
  • Beng Chin Ooi1
  • 1 National University of Singapore, Singapore 2
    Aalborg University, Denmark

2
Outline
  • Introduction
  • Problem Definition
  • Skyline Queries in MANETs
  • Optimizations on Mobile Devices
  • Experimental Studies
  • Conclusion

3
Introduction
  • Mobile Ad-hoc NETworks (MANETs)
  • A MANET is a self configuring network of mobile
    devices connected by wireless links
  • Wireless topology usually changes rapidly and
    unpredictably

price
  • Skyline Queries
  • Operator based on dominance
  • Return tuples from sets of tuples that are not
    dominated by others

quality
4
Skyline Queries in MANETs
  • Assumptions
  • Each resource-constrained device holds a portion
    of the entire dataset
  • A mobile user is only interested in data of a
    limited geographical area, though the query
    involves data stored on multiple mobile devices

5
Example
  • M1 to M4 hold different hotel relations
  • M2 is interested in cheap and good hotels within
    the circle area

6
Outline
  • Introduction
  • Problem Definition
  • Skyline Queries in MANETs
  • Optimizations on Mobile Devices
  • Experimental Studies
  • Conclusion and Future Work

7
Problem Setting
  • MANET of m mobile devices
  • M1, M2, , Mm
  • Local relation Ri on each device Mi
  • Skyline issued by a device Morg
  • id network id of query originator Morg
  • pos position of Morg
  • ddistance (from pos) of interest

8
Technical Challenges
  • Slow and unreliable wireless channels compared to
    wired connections
  • To reduce data transferred between devices
  • Resource-constrained devices
  • Storage and processing saving techniques on
    mobile devices

9
Outline
  • Introduction
  • Problem Definition
  • Skyline Queries in MANETs
  • Optimizations on Mobile Devices
  • Experimental Studies
  • Conclusion

10
Naïve Solution
  • Query originator Morg
  • Executing a local skyline query SKorg
  • Sends query to other mobile devices
  • Merges results when receiving them
  • A mobile device Mi
  • Executing a local skyline query too
  • Sends result SKi back to Morg
  • Instead of sending whole Ri

11
Discussion
  • Final skyline result SK
  • SK?USKi, SK USKi
  • FSK USKiSK
  • FSK contains all those tuples that are not in SK
    but sent between devices
  • Identify SKiSK on device Mi

U
12
Filtering Strategy
  • Any tuple tpi in SKiSK is dominated by some
    tuple(s) tpj in SK
  • Where to find such tpjs?
  • Pick from Morgs local result
  • Send as query
  • Mi filters out tuples using tpj
  • Which one to pick?
  • Dominating region

13
Dominating Region
  • The ability of tpj to dominate others
  • Tuple value
  • Data space boundaries
  • Volume of dominating region
  • VDRj?k(bk-pjk)
  • Choose from SKorg tpflt with max VDRj

14
Dominating Ability
  • Two hotel relations
  • Price range (20..200)
  • Smaller rating means better (1..10)

Relation R1
Relation R2 (Morg)
15
Estimated Dominating Region
  • Over-estimation
  • VDRj?k(maxk-pjk)
  • maxkpre-specified larger value
  • Under-estimation
  • VDRj?k(hk-pjk)
  • hklocal maximum

16
Dynamic Filtering Tuples
  • Three hotel relations
  • M4 - M3 - M1

VDR31980
VDR41960
Relation R3
Relation R4 (Morg)
Relation R1
17
Outline
  • Introduction
  • Problem Definition
  • Skyline Queries in MANETs
  • Optimizations on Mobile Devices
  • Experimental Studies
  • Conclusion

18
Dataset Storage
  • Goals
  • Space efficient
  • Local processing efficient
  • Operations
  • Spatial extent check
  • Distinct coordinates
  • Attribute value comparison
  • Floats
  • Duplicates

19
Hybrid Storage Model
  • Spatial coordinates
  • Real values
  • MBRi(xmax, ymax, xmin, ymin)
  • Attribute values
  • Ascending domains
  • IDs
  • Sort p1

Relation Ri
Sorted domains
p1
pn

20
Local Skyline Computing
  • Sptial check
  • mindist(posorg, MBRi) d
  • Skyline computing
  • Comparison of IDs instead of true values of float
    type
  • p2 to pn only
  • Update filtering tuple if necessary
  • Choose the one with larger VDR value

21
Assembly on Query Originator
  • When Morg receives SKi from others
  • Duplication elimination
  • False positive removing
  • A simple nested loop is enough
  • Comparing coordinates
  • Identify duplicates
  • Comparing attribute values
  • Identify false positive reports from both SKorg
    and SKi

22
Outline
  • Introduction
  • Problem Definition
  • Skyline Queries in MANETs
  • Optimizations on Mobile Devices
  • Experimental Studies
  • Conclusion

23
Experiment Parameters
24
Studies on Local Optimization
  • HP iPAQ h6365 pocket PC
  • MS Windows Mobile 2003
  • 200MHz TI OMAP1510 processor
  • 64MB SDRAM (55MB user accessible)
  • SuperWaba
  • Java-based open-source platform for PDA and
    smartphone applications
  • www.superwaba.org

25
Time vs Local Rel. Cardinality
  • Flat Storage vs Hybrid Storage
  • Data set Anti-Correlated vs Independent
  • HS incurs less processing cost

26
Time vs Local Dimensionality
  • Average of costs on both distributions
  • Coz they are close to each other
  • HS still performs better

27
Performance in Simulation
  • Simulated MANET
  • JiST-SWANS
  • A Jave based MANET simulator
  • http//jist.ece.cornell.edu/
  • Pentium IV desktop PC
  • MS Windows XP
  • 2.99GHz CPU
  • 1 GB memory

28
Settings
  • Device setting
  • Data partitioned and allocated to devices using a
    grid of m1/2 by m1/2
  • 1-5 queries per device
  • MANET settings
  • Total simulation time 2 hours
  • Speed range 2 unit/s 10 unit/s
  • Holding time 120 seconds
  • Wireless routing protocol AODV

29
Data Reduction Efficiency
  • Data Reduction Rate
  • SKi is the local skyline after filtering
  • Pre-tests in static setting
  • Forwarding query out recursively
  • Findings
  • No significant difference between exact VDR and
    estimated VDRs
  • Dynamic filtering is more powerful

30
Data Reduction Rate
31
Response Time - BF
  • Breadth-First query forwarding
  • Parallel
  • Time receiving answers from 80 other devices
  • Cannot ensure all devices are always reachable
    and available in MANETs

M2
M1
M3
Morg
M5
M4
Query message
Result message
32
Response Time - DF
  • Depth-First forwarding
  • Serialized
  • Query ends when originator finds all neighbors
    have processed the query

M2
M3
M1
M4
M5
Morg
Query message
Result message
33
Response Time
34
Outline
  • Introduction
  • Problem Definition
  • Skyline Queries in MANETs
  • Optimizations on Mobile Devices
  • Experimental Studies
  • Conclusion

35
Conclusion
  • Problem setting
  • MANET of lightweight devices
  • Skyline queries with spatial constraints
  • Solution highlights
  • Filtering based distributed query processing
    strategy to reduce communication cost
  • Specialized local storage and algorithm to speed
    up local processing
  • Experimentally verified performance

36
  • Thanks!
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