An information model for geographic greedy forwarding in wireless ad hoc sensor networks
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An Information Model for Geographic Greedy Forwarding in Wireless Ad-Hoc Sensor Networks. Junchao Ma, Wei Lou Dept. of Computing The HK Polytechnic University. Zhen Jiang Comp. Sci. Dept. West Chester University. Jie Wu Dept. of Computer Sci. & Eng. Florida Atlantic University & NSF.

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An information model for geographic greedy forwarding in wireless ad hoc sensor networks

An Information Model for Geographic Greedy Forwarding in Wireless Ad-Hoc Sensor Networks

Junchao Ma, Wei Lou

Dept. of Computing

The HK Polytechnic University

Zhen Jiang

Comp. Sci. Dept.

West Chester University

Jie Wu

Dept. of Computer Sci. & Eng.

Florida Atlantic University & NSF

INFOCOM'08


Outline
Outline Wireless Ad-Hoc Sensor Networks

  • Introduction

  • Our Approaches

  • Experimental Results

  • Conclusion

  • Future Work

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An information model for geographic greedy forwarding in wireless ad hoc sensor networks
Goal Wireless Ad-Hoc Sensor Networks

  • An efficient path in wireless ad-hoc sensor networks (WASNs)

    • Fewer hops and detours

    • Faster data delivery

    • More energy conserved

destination

source

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Problem
Problem Wireless Ad-Hoc Sensor Networks

  • Local minimum phenomenon (void)

    • Sparse deployment

    • Physical obstacles

    • Node failures

    • Communication jamming

    • Power exhaustion

    • Animus interference

block

void

stuck node

destination

source

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An information model for geographic greedy forwarding in wireless ad hoc sensor networks
Idea Wireless Ad-Hoc Sensor Networks

  • Information helps routing to

    • Predict the ‘void’ ahead

    • Make a slightturn early to sufficiently avoid being blocked

      • Non-detour routing, i.e., greedy forwarding without perimeter routing

    • Make a turn only if necessary

      • To keep the optimality of a straight forwarding

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Challenge 1
Challenge 1 Wireless Ad-Hoc Sensor Networks

  • Identification of the affected area of a void

    • Relative to the positions of the source and the destination

destination

destination

source

destination

source

source

destination

affected area

affected area

Case 1

Case 2

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Challenge 2
Challenge 2 Wireless Ad-Hoc Sensor Networks

  • Mutual impact of void areas

    • Global optimization achieved by neighborhood optimizations

      • No routing table, flooding, or broadcasting

      • Routing decision at each intermediate node

      • Neighbor information collection and distribution

destination

area of mutual impact

source

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Challenge 3
Challenge 3 Wireless Ad-Hoc Sensor Networks

  • Unstructured WASNs

    • Hard to ensure whether the forwarding still achievable ahead

destination

?

source

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Illustration
Illustration Wireless Ad-Hoc Sensor Networks

What kind of information and how to conduct a forecast?

No global information

New York (destination)

Information exchanged with next light

traffic prediction

West Chester (source)

light control related to travel destination

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Related work
Related Work Wireless Ad-Hoc Sensor Networks

  • “Dead end” model

    • No optimization

  • Boundary model

    • No global optimization

  • Hull algorithm, or turning angle model

    • No consideration of the relative positions

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Our approaches
Our Approaches Wireless Ad-Hoc Sensor Networks

  • Tradeoff between routing adaptivity and structure regularity

  • Safety information for such a forwarding

  • Information based forwarding (SLGF routing)

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Tradeoff
Tradeoff Wireless Ad-Hoc Sensor Networks

  • A forwarding with infrastructure in WASNs

    • LAR2:

      • Forwarding to a neighbor that is closer to the destination

    • We adopt LAR1

      • Forwarding limited in the so-called request zone

destination

destination

forwarding candidate

LAR 1

LAR 2

request zone

current node

current node

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Safety information
Safety Information Wireless Ad-Hoc Sensor Networks

  • Inspired by the safety model in 2-D mesh networks

  • An unsafe area contains nodes that definitely causing routing detour.

  • Constructed by a labeling process via information exchanges among neighbors.

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Details of the labeling process
Details of the Labeling Process Wireless Ad-Hoc Sensor Networks

  • Unsafe node

    • A node without any neighbor in the request zone

    • A node without any safe neighbor in the request zone

  • Unsafe area

    • Connected unsafe nodes

    • Estimated as a rectangle at an unsafe node.

  • 4 Different types of unsafe status

    • Due to 4 different types of request zones

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An example of type i safety information
An Example of Type-I Safety Information Wireless Ad-Hoc Sensor Networks

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Slgf routing
SLGF Routing Wireless Ad-Hoc Sensor Networks

  • Four phases conducted in the order

    • Enforced forwarding

    • Safe forwarding

    • Perimeter routing (for making a slight turn)

    • Retreating (in the opposite direction)

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Simulation
Simulation Wireless Ad-Hoc Sensor Networks

  • To verify whether

    LAR1(-) + Safety Info. (-) + Info. based routing (+) is better than boundary info. based routing.

  • Forwarding routings

    • GF (LAR2 + boundary information)

    • LGF (LAR1)

    • SLGF (LAR1 + safety information)

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An information model for geographic greedy forwarding in wireless ad hoc sensor networks

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Result summary
Result Summary Wireless Ad-Hoc Sensor Networks

  • Cost

    safety information < boundary information

  • Routing success

    GF = LGF = SLGF

  • Routing path

    LGF < GF << SLGF

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Conclusion
Conclusion Wireless Ad-Hoc Sensor Networks

  • Unicast routing but neighborcast information construction

  • Tradeoff between routing adaptivity and information model cost

  • Mutual impact of void areas

  • Better forwarding routing to achieve more straight paths

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Future work
Future Work Wireless Ad-Hoc Sensor Networks

  • New balance point of the tradeoff between routing adaptivity and information model cost

  • More accurate information

  • Better forwarding routing to achieve more straight path

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Questions

Questions? Wireless Ad-Hoc Sensor Networks

Thank you!

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