an information model for geographic greedy forwarding in wireless ad-hoc sensor networks
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
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An Information Model for Geographic Greedy Forwarding in Wireless Ad-Hoc Sensor Networks
Zhen JiangComp. Sci. Dept.
West Chester University
Junchao Ma, Wei LouDept. of Computing
The HK Polytechnic University
Jie WuDept. of Computer Sci. & Eng.
Florida Atlantic University & NSF
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Outline Introduction Our Approaches Experimental Results Conclusion Future Work
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Goal An efficient path in wireless ad-hoc
sensor networks (WASNs) Fewer hops and detours Faster data delivery More energy conserved
source
destination
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Problem Local minimum phenomenon (void)
Sparse deployment Physical obstacles Node failures Communication jamming Power exhaustion Animus interference
source
destination
voidblock
stuck node
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Idea Information helps routing to
Predict the ‘void’ ahead Make a slight turn 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 Identification of the affected area of
a void Relative to the positions of the source and
the destination
source
destination
destination
source
destination
source
destination
Case 1 Case 2
affected area affected area
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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
source
destination
area of mutual impact
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Challenge 3 Unstructured WASNs
Hard to ensure whether the forwarding still achievable ahead
destination
source
?
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Illustration
West Chester (source)
New York (destination)No global information
light control related to travel destination
Information exchanged with next light
traffic prediction
What kind of information and how to conduct a forecast?
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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 Tradeoff between routing adaptivity and
structure regularity
Safety information for such a forwarding
Information based forwarding (SLGF routing)
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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
current node
destination
current node
destinationforwarding candidate
request zone
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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 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
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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 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 Cost
safety information < boundary information Routing success
GF = LGF = SLGF Routing path
LGF < GF << SLGF
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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 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?
Thank you!