density-aware hop-count localization (dhl) in wireless sensor networks with variable density
DESCRIPTION
Density-Aware Hop-Count Localization (DHL) in Wireless Sensor Networks with Variable Density. Sau Yee Wong 1,2 , Joo Chee Lim 1 , SV Rao 1 , Winston KG Seah 1 1 Communications and Devices Division, Institute for Infocomm Research 2 National University of Singapore IEEE WCNC 2005. - PowerPoint PPT PresentationTRANSCRIPT
Density-Aware Hop-Count Localization (DHL) in Wireless Sensor Networks with Variable Density
Sau Yee Wong1,2, Joo Chee Lim1, SV Rao1, Winston KG Seah1
1Communications and Devices Division, Institute for Infocomm Research2National University of SingaporeIEEE WCNC 2005
Shao-Chun Wang
Outline
Introduction Background and Related Work Density-Aware Hop-Count
Localization (DHL) Simulation Result Conclusion
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Introduction-(cont.)-
Hop-count localization algorithm simple sensor networks are multi-hop sensors usually have low mobility packet size is small and constant
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ReferenceNode
Introduction-(cont.)-
conventional hop-count localization algorithms only provide good location estimation if the node distribution in the network is dense and uniform
The node distribution in a sensor network is not always uniform terrain contour hostile environment
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Introduction
Goal improve the accuracy of location
estimation when the node distribution is non-uniform
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Background and Related Work-(cont.)-
hop-count localization algorithm triangulation
distance between a Reference Node (RN) and any node can be estimated by D = HC x R D : distance HC : min. hop-count from the RN R : transmission range
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Background and Related Work-(cont.)-
Case A
Reference Node
Forwarding Node
Destination Node
Node
Transmission Range : R
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Background and Related Work-(cont.)-
Case B
Reference Node
Forwarding Node
Destination Node
Node
Transmission Range : R
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Background and Related Work-(cont.)-
Case C
Reference Node
Forwarding Node
Destination Node
Node
Transmission Range : R
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Background and Related Work-(cont.)-
Case D
Reference Node
Forwarding Node
Destination Node
Node
Transmission Range : R
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Background and Related Work-(cont.)-
R 2R 3R 4R
ActualDistance
Case A
Case B
Case C
Case D
EstimatedDistance
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Background and Related Work-(cont.)-
“Ad hoc positioning system (APS)” Globecom 2001 DV-Hop
Background and Related Work-(cont.)-
DV-Hop
R2
R1R3
A
R1,R2,R3:Refernece Node
1.Flooding Beacon:Location Information
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Background and Related Work-(cont.)-
DV-Hop
R2
R1R3
A
2.Each node maintains a table:{ Xi , Yi , hi }hi: Min. Hop Count
{ X1 , Y1 , 3 }
{ X2 , Y2 , 2 }
{ X3 , Y3 , 3 }
{ X1 , Y1 , 0 }{ X2 , Y2 , 2 }{ X3 , Y3 , 6 }
{ X1 , Y1 , 2 }{ X2 , Y2 , 0 }{ X3 , Y3 , 5 }
{ X1 , Y1 , 5 }{ X2 , Y2 , 6 }{ X3 , Y3 , 0 }
Background and Related Work-(cont.)-
DV-Hop
R2
R1R3
A
3.Reference Node estimates anaverage size for one hop
100m
75m40m
42.1652
4075
5.1726
40100
90.15
56
75100
Background and Related Work-(cont.)-
DV-Hop
R2
R1R3
A
4.Flooding Beacon:Average size for one hop
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Background and Related Work-(cont.)-
DV-Hop
R2
R1R3
A
5.Estimate distance to the three Reference Nodes
R1: 3 x 16.42R2: 2 x 16.42R3: 3 x 16.42
6.Node A perform a triangulation to get its location
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Background and Related Work
The distance per hop is greater in dense regions and smaller in sparse regions
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Density-Aware Hop-Count Localization-(cont.)-
Assumptions network is connected sensors have low mobility each node is assumed to know its
number of neighbors
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Density-Aware Hop-Count Localization-(cont.)-
Local density is defined as the number of neighbors Nngbr
Range ratio the ratio of hop-distance to the
transmission range μ Σμ
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hop-distance
Density-Aware Hop-Count Localization-(cont.)-
Density categories p < Nngbr < q
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Density-Aware Hop-Count Localization-(cont.)-
μ=0.7
μ=0.8
μ=0.6
μ=0.6
Reference Node
Forwarding Node
Destination Node
Node
Transmission Range : R
Low density μ=0.6Medium density μ=0.7High density μ=0.8
Σμ=
Σμ=
Σμ= Σμ=A
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Density-Aware Hop-Count Localization-(cont.)-
μ=0.7
μ=0.8
μ=0.6
μ=0.6
Reference Node
Forwarding Node
Destination Node
Node
Transmission Range : R
Low density μ=0.6Medium density μ=0.7High density μ=0.8
1.Flooding Beacon:•Location Information
Σμ=
Σμ=
Σμ= Σμ=A
Shao-Chun Wang
2. Σμ + μ
Density-Aware Hop-Count Localization-(cont.)-
μ=0.7
μ=0.8
μ=0.6
μ=0.6
Reference Node
Forwarding Node
Destination Node
Node
Transmission Range : R
Low density μ=0.6Medium density μ=0.7High density μ=0.8
1.Flooding Beacon:•Location Information
Σμ= 0.8
Σμ=
Σμ= Σμ=A
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2. Σμ + μ
Density-Aware Hop-Count Localization-(cont.)-
Hop information update method
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Density-Aware Hop-Count Localization-(cont.)-
μ=0.7
μ=0.8
μ=0.6
μ=0.6
Reference Node
Forwarding Node
Destination Node
Node
Transmission Range : R
Low density μ=0.6Medium density μ=0.7High density μ=0.8
3.forwards accumulated range ratio to their neighbors
Σμ= 0.8
Σμ=1.5
Σμ=2.1 Σμ=2.7A
Shao-Chun Wang
Density-Aware Hop-Count Localization
μ=0.7
μ=0.8
μ=0.6
μ=0.6
4.Estimate distance to the Reference NodesD = 2.9 x R
Σμ= 0.8
Σμ=1.5
Σμ=2.1 Σμ=2.7
Reference Node
Forwarding Node
Destination Node
Node
Transmission Range : R
Low density μ=0.6Medium density μ=0.7High density μ=0.8
5.Node A perform a triangulation to get its location
A
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Simulation Results
Range ratio determination Distance accuracy comparisons Position accuracy comparisons Overhead comparisons
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Simulation Results-Range Ratio Determination (cont.)-
50m x 50m square area Transmission range : 5m Ratio range : 0.1 – 0.9
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Simulation Results-Range Ratio Determination (cont.)-
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Simulation Results-Range Ratio Determination-
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Simulation Results-Distance Accuracy Comparisons-
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Simulation Results-Position Accuracy Comparisons-
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Simulation Results-Overhead Comparisons (cont.)-
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Simulation Results-Overhead Comparisons-
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Conclusion
We described a DHL method that address network non-uniformity by using range ratios improve localization accuracy lower packet transmission overhead
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