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

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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
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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

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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
• ltx, y, p1, p2, , pngt
• Skyline issued by a device Morg
• ltid, posorg, dgt
• id network id of query originator Morg
• pos position of Morg
• ddistance (from pos) of interest

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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

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Outline

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

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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

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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
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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 ltid, posorg, d, tpjgt as query
• Mi filters out tuples using tpj
• Which one to pick?
• Dominating region

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Dominating Region

• The ability of tpj to dominate others
• Tuple value ltpj1, pj2, , pjngt
• Data space boundaries
• Volume of dominating region
• VDRj?k(bk-pjk)
• Choose from SKorg tpflt with max VDRj

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Dominating Ability

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

Hotel Price Rating
h11 20 7
h12 40 5
h13 80 7
h14 80 4
h15 100 7
h16 100 3
Hotel Price Rating
h21 60 3
h22 80 2
h23 120 1
h24 140 2
h25 100 4
VDR
(200-60)(10-3)980
(200-80)(10-2)960
(200-120)(10-1)720
-
-
Relation R1
Relation R2 (Morg)
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Estimated Dominating Region

• Over-estimation
• VDRj?k(maxk-pjk)
• maxkpre-specified larger value
• Under-estimation
• VDRj?k(hk-pjk)
• hklocal maximum

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Dynamic Filtering Tuples

• Three hotel relations
• M4 -gt M3 -gt M1

VDR31980
VDR41960
Hotel Price Rating
h31 60 3
h32 80 5
h33 120 4
Hotel Price Rating
h41 80 2
h42 120 1
h43 140 2
Hotel Price Rating
h11 20 7
h12 40 5
h13 80 7
h14 80 4
h15 100 7
h16 100 3
Relation R3
Relation R4 (Morg)
Relation R1
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Outline

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

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Dataset Storage

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

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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
x y p1 pn
1.331 103.67 0 2
1.329 103.59 1 0
1.412 103.77 2 j

1.429 103.95 k 3
v0
v1

vk
v0
v1

vj

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Local Skyline Computing

• Sptial check
• mindist(posorg, MBRi) gt 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

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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

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Outline

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

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Experiment Parameters
Number of mobile device 32, 42, , 102
Cardinality of global reln 100K, 200K, , 1000K
Cardinality of local reln 10K, 20K, , 100K
Local storage model Flat, Hybrid
Number of non-spatial attr 2, 3, 4, 5
Non-spatial attri range 0.0, 9.9, 0,1000
Spatial extent of global reln 1000 X 1000
Attribute distribution Indep., Anti-Correl.
Query distance of interest 100, 250, 500
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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

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Time vs Local Rel. Cardinality

• Flat Storage vs Hybrid Storage
• Data set Anti-Correlated vs Independent
• HS incurs less processing cost

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Time vs Local Dimensionality

• Average of costs on both distributions
• Coz they are close to each other
• HS still performs better

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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

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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

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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

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Data Reduction Rate
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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
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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
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Response Time
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Outline

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

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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

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• Thanks!