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

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outline
Outline
  • Introduction
  • Our Approaches
  • Experimental Results
  • Conclusion
  • Future Work

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slide3
Goal
  • 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
  • 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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slide5
Idea
  • 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
  • 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
  • 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
  • Unstructured WASNs
    • Hard to ensure whether the forwarding still achievable ahead

destination

?

source

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illustration
Illustration

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
  • “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
  • Tradeoff between routing adaptivity and structure regularity
  • Safety information for such a forwarding
  • Information based forwarding (SLGF routing)

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tradeoff
Tradeoff
  • 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
  • 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
  • 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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slgf routing
SLGF Routing
  • 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
  • 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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result summary
Result Summary
  • Cost

safety information < boundary information

  • Routing success

GF = LGF = SLGF

  • Routing path

LGF < GF << SLGF

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conclusion
Conclusion
  • 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
  • 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?

Thank you!

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