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 Shao-Chun Wang

Outline Introduction Background and Related Work Density-Aware Hop-Count Localization (DHL) Simulation Result Conclusion Shao-Chun Wang

Introduction -(cont.)- Hop-count localization algorithm simple sensor networks are multi-hop sensors usually have low mobility packet size is small and constant Shao-Chun Wang Reference Node

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 Shao-Chun Wang

Introduction Goal improve the accuracy of location estimation when the node distribution is non-uniform Shao-Chun Wang

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 Shao-Chun Wang

Background and Related Work -(cont.)- Case A Reference Node Forwarding Node Destination Node Node Transmission Range : R Shao-Chun Wang

Background and Related Work -(cont.)- Case B Reference Node Forwarding Node Destination Node Node Transmission Range : R Shao-Chun Wang

Background and Related Work -(cont.)- Case C Reference Node Forwarding Node Destination Node Node Transmission Range : R Shao-Chun Wang

Background and Related Work -(cont.)- Case D Reference Node Forwarding Node Destination Node Node Transmission Range : R Shao-Chun Wang

Background and Related Work -(cont.)- R2R3R4R Actual Distance Case A Case B Case C Case D Estimated Distance Shao-Chun Wang

Background and Related Work -(cont.)- “ Ad hoc positioning system (APS) ” Globecom 2001 DV-Hop

Background and Related Work -(cont.)- DV-Hop R2 R1 R3 A R1,R2,R3:Refernece Node 1.Flooding Beacon: Location Information Shao-Chun Wang

Background and Related Work -(cont.)- DV-Hop R2 R1 R3 A 2.Each node maintains a table: { X i, Y i, h i } h i : Min. Hop Count { X 1, Y 1, 3 } { X 2, Y 2, 2 } { X 3, Y 3, 3 } { X 1, Y 1, 0 } { X 2, Y 2, 2 } { X 3, Y 3, 6 } { X 1, Y 1, 2 } { X 2, Y 2, 0 } { X 3, Y 3, 5 } { X 1, Y 1, 5 } { X 2, Y 2, 6 } { X 3, Y 3, 0 }

Background and Related Work -(cont.)- DV-Hop R2 R1 R3 A 3.Reference Node estimates an average size for one hop 100m 75m 40m

Background and Related Work -(cont.)- DV-Hop R2 R1 R3 A 4.Flooding Beacon: Average size for one hop Shao-Chun Wang

Background and Related Work -(cont.)- DV-Hop R2 R1 R3 A 5.Estimate distance to the three Reference Nodes R1: 3 x R2: 2 x R3: 3 x Node A perform a triangulation to get its location Shao-Chun Wang

Background and Related Work The distance per hop is greater in dense regions and smaller in sparse regions Shao-Chun Wang

Density-Aware Hop-Count Localization -(cont.)- Assumptions network is connected sensors have low mobility each node is assumed to know its number of neighbors Shao-Chun Wang

Density-Aware Hop-Count Localization -(cont.)- Local density is defined as the number of neighbors N ngbr Range ratio the ratio of hop-distance to the transmission range μ Σμ Shao-Chun Wang hop-distance

Density-Aware Hop-Count Localization -(cont.)- Density categories p < N ngbr < q Shao-Chun Wang

Density-Aware Hop-Count Localization -(cont.)- μ=0.7 μ=0.8 μ=0.6 Reference Node Forwarding Node Destination Node Node Transmission Range : R Low density μ=0.6 Medium density μ=0.7 High density μ=0.8 Σμ=Σμ= Σμ=Σμ= Σμ=Σμ= Σμ=Σμ= A Shao-Chun Wang

Density-Aware Hop-Count Localization -(cont.)- μ=0.7 μ=0.8 μ=0.6 Reference Node Forwarding Node Destination Node Node Transmission Range : R Low density μ=0.6 Medium density μ=0.7 High density μ=0.8 1.Flooding Beacon: Location Information Σμ=Σμ= Σμ=Σμ= 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 Reference Node Forwarding Node Destination Node Node Transmission Range : R Low density μ=0.6 Medium density μ=0.7 High density μ=0.8 1.Flooding Beacon: Location Information Σμ= 0.8 Σμ=Σμ= Σμ=Σμ= Σμ=Σμ= A Shao-Chun Wang 2. Σμ + μ

Density-Aware Hop-Count Localization -(cont.)- Hop information update method Shao-Chun Wang

Density-Aware Hop-Count Localization -(cont.)- μ=0.7 μ=0.8 μ=0.6 Reference Node Forwarding Node Destination Node Node Transmission Range : R Low density μ=0.6 Medium density μ=0.7 High density μ=0.8 3.forwards accumulated range ratio to their neighbors Σμ= 0.8 Σμ=1.5 Σμ=2.1 Σμ=2.7 A Shao-Chun Wang

Density-Aware Hop-Count Localization μ=0.7 μ=0.8 μ=0.6 4.Estimate distance to the Reference Nodes D = 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=0.6 Medium density μ=0.7 High density μ=0.8 5.Node A perform a triangulation to get its location A Shao-Chun Wang

Simulation Results Range ratio determination Distance accuracy comparisons Position accuracy comparisons Overhead comparisons Shao-Chun Wang

Simulation Results -Range Ratio Determination (cont.) - 50m x 50m square area Transmission range : 5m Ratio range : 0.1 – 0.9 Shao-Chun Wang

Simulation Results -Range Ratio Determination (cont.) - Shao-Chun Wang

Simulation Results -Range Ratio Determination- Shao-Chun Wang

Simulation Results -Distance Accuracy Comparisons- Shao-Chun Wang

Simulation Results -Position Accuracy Comparisons- Shao-Chun Wang

Simulation Results -Overhead Comparisons (cont.)- Shao-Chun Wang

Simulation Results -Overhead Comparisons- Shao-Chun Wang

Conclusion We described a DHL method that address network non-uniformity by using range ratios improve localization accuracy lower packet transmission overhead Shao-Chun Wang