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Skyline Queries Against Mobile Lightweight Devices in MANETs. Zhiyong Huang 1 Christian S. Jensen 2 Hua Lu 1 Beng Chin Ooi 1 1 National University of Singapore, Singapore 2 Aalborg University, Denmark. Outline. Introduction Problem Definition Skyline Queries in MANETs

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Skyline queries against mobile lightweight devices in manets l.jpg

Skyline Queries Against Mobile Lightweight Devices in MANETs

Zhiyong Huang1

Christian S. Jensen2

Hua Lu1

Beng Chin Ooi1

1 National University of Singapore, Singapore2 Aalborg University, Denmark


Outline l.jpg
Outline

  • Introduction

  • Problem Definition

  • Skyline Queries in MANETs

  • Optimizations on Mobile Devices

  • Experimental Studies

  • Conclusion


Introduction l.jpg
Introduction

  • Skyline query

    • Operator based on dominance

  • MANET

    • Self-organizing, wireless mobile ad-hoc networks

    • Physical environment of this work

      • Lightweight devices


Skyline queries in manets l.jpg
Skyline Queries in MANETs

  • Assumptions

    • Each resource-constrained device holds a portion of the entire dataset

    • Devices communicate through MANET

    • A mobile user is only interested in data of a limited geographical area, though the query involves data stored on multiple mobile devices


Example l.jpg

M1

M2

M3

M4

Example

  • M1 to M4 hold different hotel relations

  • M2 is interested in cheap and good hotels within the circle area


Outline6 l.jpg
Outline

  • Introduction

  • Problem Definition

  • Skyline Queries in MANETs

  • Optimizations on Mobile Devices

  • Experimental Studies

  • Conclusion and Future Work


Problem setting l.jpg
Problem Setting

  • MANET of m mobile devices

    • {M1, M2, …, Mm}

  • Local relation Ri on each device Mi

    • <x, y, p1, p2, …, pn>

  • Skyline issued by a device Morg

    • <id, posorg, d>

      • id: network id of query originatorMorg

      • pos: position of Morg

      • d:distance (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


Straightforward strategy l.jpg
Straightforward Strategy

  • 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


Discussion l.jpg
Discussion

  • Final skyline result: SK

  • SK≠USKi, SK USKi

  • FSK = USKi–SK

  • FSK contains all those tuples that are not in SK but sent between devices

  • Identify SKi–SK on device Mi

    • Inspiration of semi-join

U|


Filtering strategy l.jpg
Filtering Strategy

  • Any tuple tpi in SKi–SK is dominated by some tuple(s) tpj in SK

  • Where to find such tpjs?

    • Pick from Morg’s local result

    • Send <id, posorg, d, tpj> as query

    • Mi filters out tuples using tpj

  • Which one to pick?

    • Dominating region


Dominating region l.jpg

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Dominating Region

  • The ability of tpj to dominate others

    • Tuple value <pj1, pj2, …, pjn>

    • Data space boundaries

  • Volume of dominating region

    • VDRj=∏k(bk-pjk)

  • Choose from SKorgtpflt with max VDRj

    • Indep. distribution


Dominating ability l.jpg
Dominating Ability

  • Two hotel relations

    • Price range (20..200)

    • Smaller rating means better (1..10)

Relation R1

Relation R2 (Morg)


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Estimated Dominating Region

  • Over-estimation

    • VDRj=∏k(maxk-pjk)

    • maxk:pre-specified larger value

  • Under-estimation

    • VDRj=∏k(hk-pjk)

    • hk:local maximum


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Dynamic Filtering Tuples

  • Three hotel relations

    • M4 -> M3 -> M1

VDR31=980

VDR41=960

Relation R3

Relation R4 (Morg)

Relation R1


Query log mechanism l.jpg
Query Log Mechanism

  • To avoid the same query more than once on any device Mi

  • Add a tag cnt to query issued by Morg

    • <id, cnt, posorg, d, tpflt>

  • Mi records/checks/updates <id, cnt>

    • Processes and forwards only cntlog<cnt

  • cnt can be a byte to save cost

    • A device can issue 256 queries

    • Reset after a period, say one day


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Outline

  • Introduction

  • Problem Definition

  • Skyline Queries in MANETs

  • Optimizations on Mobile Devices

  • Experimental Studies

  • Conclusion


Dataset storage l.jpg
Dataset Storage

  • Goals

    • Space efficient

    • Local processing efficient

  • Operations

    • Spatial extent check

      • Distinct coordinates

    • Attribute value comparison

      • Floats

      • Duplicates


Hybrid storage model l.jpg
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


Local skyline computing l.jpg
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


Assembly on query originator l.jpg
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


Outline23 l.jpg
Outline

  • Introduction

  • Problem Definition

  • Skyline Queries in MANETs

  • Optimizations on Mobile Devices

  • Experimental Studies

  • Conclusion



Studies on local optimization l.jpg
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 Cardinality

  • Flat Storage vs Hybrid Storage

  • 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


Performance in simulation l.jpg
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


Settings l.jpg
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


Data reduction efficiency l.jpg
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



Response time bf l.jpg
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


Response time df l.jpg
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



Query message count l.jpg
Query Message Count

  • Only mobile device number affects the query message count obviously

  • Better performance of BF is not free


Outline36 l.jpg
Outline

  • Introduction

  • Problem Definition

  • Skyline Queries in MANETs

  • Optimizations on Mobile Devices

  • Experimental Studies

  • Conclusion


Conclusion l.jpg
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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Q & A

Thanks!


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