Indoor Localization using Local Node Density    in Ad-Hoc Wireless Sensor Networks                   Proyecto Final de Car...
Table of Contents1.     Objective and thesis contribution2.     Wireless Sensor Networks (WSNs)3.     Problem statement4. ...
Thesis contribution Objective:           Deployment and performance characterization of indoor distributed location      ...
Table of Contents1.     Objectives and thesis contribution2. Wireless Sensor Networks (WSNs)3.     Problem statement4.    ...
Wireless Sensor Networks (WSNs)   Collection of autonomous, spatially distributed devices.   Nodes have sensing capabili...
Table of Contents1.     Objectives and thesis contribution2.     Wireless Sensor Networks (WSNs)3. Problem statement4.    ...
Problem statement Goal:       Determine the location of individual sensor       nodes without relying on external infrastr...
Table of Contents1.     Objectives and thesis contribution2.     Wireless Sensor Networks (WSNs)3.     Problem statement4....
Localization in WSNs: Overview    Area of intense research activity in the past years.    Broad spectrum of location techn...
Measurement Techniques1.    Distance related          Received Signal Strength Indicator (RSSI)          Time of Arrival (...
Location Systems for WSNs        One-hop                                                            Multihop              ...
Localization in WSNs:                                      General trends     Complexity/cost and accuracy tradeoff       ...
Table of Contents1.     Objectives and thesis contribution2.     Wireless Sensor Networks (WSNs)3.     Problem statement4....
Evaluated algorithms     Common features:          Truly distributed  no external infrastructure or centralized processin...
Algorithms overview     Range-based:              Local Node Density-based (LND)              DV-Dist              RSSI-ba...
LND Algorithm    Phase 1a. DIN internodal range          Local node density information to estimate distances          Exe...
LND Algorithm    Phase 1a. DIN internodal range          Local node density information to estimate distances          Exe...
LND Algorithm    Phase 1a. DIN internodal range          Local node density information to estimate distances          Exe...
LND Algorithm    Phase 1a. DIN internodal range          Local node density information to estimate distances          Exe...
LND Algorithm    Phase 1a. DIN internodal range          Local node density information to estimate distances          Exe...
LND Algorithm    Phase 1b. Initial node-to-anchor distance estimation (Sum-dist)          Flood connectivity and distance ...
LND Algorithm    Phase 1b. Initial node-to-anchor distance estimation (Sum-dist)                               Flooding pr...
LND Algorithm    Phase 1b. Initial node-to-anchor distance estimation (Sum-dist)                               Flooding pr...
LND Algorithm    Phase 1b. Initial node-to-anchor distance estimation (Sum-dist)                               Flooding pr...
LND Algorithm    Phase 1c. Factor Correction Hop (FCH)      1. Anchors capture network propagation error in correction fac...
LND Algorithm    Phase 2. Initial node positions via multilateration          Computational method to solve system of line...
LND Algorithm    Phase 3. Positioning Iterative Vector (PIV) refinement          Increase accuracy of node position estima...
LND Algorithm – PIV Example                                                                                  Execution    ...
LND Algorithm – PIV Example                                                        5   (x0,y0) = (5,9)                    ...
LND Algorithm – PIV Example                                                        5   (x0,y0) = (5,9)                    ...
Alternative hop-by-hop algorithms     DV-Dist:          Simplified version of LND (Sum-dist ≈ DV-Dist, FCH suppressed).   ...
RSSI-based algorithms            Range estimation: Relate RSSI and distance to sender.                                    ...
RSSI-based algorithms     Use RSSI empirical data to yield 2 approximation range functions (MatLab):                      ...
Table of Contents1.     Objectives and thesis contribution2.     Wireless Sensor Networks (WSNs)3.     Problem statement4....
Simulation environment    Self-implemented C-based simulator.    Simplified radio propagation: circular transmission model...
LND simulation results                                                            Phase 1a: DIN                           ...
LND simulation results Phase 1c: FCH    Tackles undershooting.    Avg. improvement not ensured  robust (?)    Distance mi...
Simulation performance comparison    Algorithms: LND, DV-Dist, DV-Hop.    Phase 1. Node-to-anchor distance estimation     ...
Simulation performance comparison                                                                        Phase 2. Initial ...
Simulation performance comparison            Phase 3. PIV iterative refinement (it. 200)                   Equalize perfor...
Table of Contents1.     Objectives and thesis contribution2.     Wireless Sensor Networks (WSNs)3.     Problem statement4....
Testbed setup    8 x 9m indoor area (seminar room).    Network configuration: uniform, horseshoe.    Nº unknowns (N): 50, ...
Overview horseshoe configuration                                                                                          ...
Implementation on real WSN hardware    Artificial circular transmission radio (R ≈ 3m):          RSSI threshold of 33 (-42...
Phase 1a. Internodal ranging (DIN, RSSI1, RSSI1)    DIN noticeably more accurate (>50%) and precise than RSSI-based method...
Phase 1a. Internodal ranging (DIN, RSSI1, RSSI1)    DIN: experimental vs simulation  performance degradation (≈0.5m).    ...
Phase 1a. Internodal ranging (DIN, RSSI1, RSSI1)    Bias analysis          DIN: almost symmetric error distribution around...
Phase 1a. Internodal ranging (DIN, RSSI1, RSSI1)    Error spatial distribution: greater at the edges of coverage area.    ...
Phase 1b-c. Node-to-anchor ranges                (DV-Hop, DV-Dist, Sum-dist/FCH, RSSI1, RSSI1)    RSSI-based       ✗      ...
Phase 1b-c. NTA range error per node                           a) DV-Hop                                                  ...
Phase 1b-c. Range error distribution                           a) DV-Hop                                                  ...
Phase 2. Initial node positionsHop-by-hop algorithms    DV-Hop       ✗          Poorest performer. Highest misplacement 2....
Phase 2. Simulation vs Experimental    Uniform: pronounced performance gap (1-3m).    Horseshoe: nodes at edges benefit fr...
Phase 2. Correlation NTA inaccuracy – node                   misplacement                     a) DV-Hop                   ...
Phase 2. Position error spatial distribution                           a) DV-Hop                                          ...
Phase 3. PIV iterative position improvement  Algorithms: DV-Hop, DV-Dist, LND.  30 iterations.  2 evaluation scenarios:   ...
Phase 3. PIV iterative position improvement  Comparable improvements (%) in algorithms accross experiments.  Determinant f...
Phase 3. PIV improvement per node                           a) DV-Hop                                                 b) D...
Phase 3. PIV improvement spatial distribution                   a) DV-Hop – Initial pos. error                   b) DV-Hop...
Table of Contents1.     Objectives and thesis contribution2.     Wireless Sensor Networks (WSNs)3.     Problem statement4....
Conclusions   Simulation:         No best performer in all scenarios: selection dependent on network conditions         (c...
Future Work    Extensive simulation over ns-2 or OMNet++ discrete event platforms.    Determine optimal context factors fo...
References[1] R. Want, A. Hopper, V. Falcao, and J. Gibbons. The active badge location system. ACM Trans. Inf. Syst., 10(1...
References[12] X. Ji and H. Zha. Multidimensional scaling based sensor positioning algorithms in wireless sensor networks....
References[21] X. Sheng, Yu-Hen Hu, and P. Ramanathan. Distributed particle filter with gmm approximation for multiple tar...
Thank you for your attention.Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks   Joaquín Gon...
Questions.Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks   Joaquín González Guerrero   67
Additional supporting slides.Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks   Joaquín Gon...
ScatterWeb Modular Sensor Board          Table 1. Key features of the ScatterWeb Modular Sensor Board (MSB-430).Indoor Loc...
Empirical analysis of FCH effectivity      Figure 30. Analysis of FCH correction procedure effectivity . Horseshoe configu...
Analysis of DV-Hop effectivity          Figure 31. Analysis of DV-Hop calibration effectivity . Uniform configuration usin...
Extended ranges – Hop-by-hop methods                   a) Absolute error                                                  ...
LND algorithm power cost  Estimates dependent on:        Network connectivity c (avg. Neighbours/node).        Nº deployed...
LND algorithm power cost                                Table 3. Computational costs of the LND localization algorithm.   ...
LND algorithm power cost   CC1020 current consumption (868MHz transmit/receive mode)         Single broadcast packet trans...
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Indoor Localization Using Local Node Density In Ad Hoc WSNs

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Presentation for Master Thesis "Indoor Localization Using Local Node Density In Ad Hoc WSNs", research supported by Free University Berlin. Coordinators: Freddy Lopez Villafuerte, Gianluca Cornetta.

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Indoor Localization Using Local Node Density In Ad Hoc WSNs

  1. 1. Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Proyecto Final de Carrera Ingeniería de Telecomunicación Ingeniería Técnica en Informática de Sistemas Joaquín González Guerrero2. Octubre. 2009 Escuela Politécnica Superior Universidad San Pablo CEU
  2. 2. Table of Contents1. Objective and thesis contribution2. Wireless Sensor Networks (WSNs)3. Problem statement4. State of the Art: Location Systems for WSNs5. Localization algorithms overview6. Simulation7. Experimental evaluation8. Conclusions9. Future WorkIndoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 2
  3. 3. Thesis contribution Objective: Deployment and performance characterization of indoor distributed location algorithms for ad-hoc wireless sensor networks. Contributions: Detailed study of indoor positioning system based on Radio Signal Strength (RSSI) range estimation. First implementation and performance evaluation of novel Local Node Density-based (LND) algorithm using simulation and real hardware. Exhaustive comparison of LND against two distributed positioning algorithms (DV-Hop, DV-Dist) over single self-developed simulation platform. Quantitative performance analysis of five distributed positioning alternatives in real indoor testbed environment. Computational, communication and power cost associated to LND algorithm.Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 3
  4. 4. Table of Contents1. Objectives and thesis contribution2. Wireless Sensor Networks (WSNs)3. Problem statement4. State of the Art: Location Systems for WSNs5. Localization algorithms overview6. Simulation7. Experimental evaluation8. Conclusions9. Future WorkIndoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 4
  5. 5. Wireless Sensor Networks (WSNs) Collection of autonomous, spatially distributed devices. Nodes have sensing capabilities. Can communicate with each other to establish a network. Resources limitations: size, cost, energy, computation, memory. Applications: Monitor physical conditions Agriculture control, species monitoring Forest fire surveillance Detect structural damage Early detection of leakagesIndoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 5
  6. 6. Table of Contents1. Objectives and thesis contribution2. Wireless Sensor Networks (WSNs)3. Problem statement4. State of the Art: Location Systems for WSNs5. Localization algorithms overview6. Simulation7. Experimental evaluation8. Conclusions9. Future WorkIndoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 6
  7. 7. Problem statement Goal: Determine the location of individual sensor nodes without relying on external infrastructure. GPS unsuitable: unrealistically high costs, coverage problems indoors. WSNS: optimal alternative  non-obstrusive, infrastructure-free and low-cost implementation. Figure 1. Structural damage detection. Motivation: A myriad of applications rely on location data to perform their tasks. Physical measurements meaningless without associated origin position. Geographic and context-based routing protocols. Figure 2. Forest fire surveillanceIndoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 7
  8. 8. Table of Contents1. Objectives and thesis contribution2. Wireless Sensor Networks (WSNs)3. Problem statement4. State of the Art: Location Systems for WSNs5. Localization algorithms overview6. Simulation7. Experimental evaluation8. Conclusions9. Future WorkIndoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 8
  9. 9. Localization in WSNs: Overview Area of intense research activity in the past years. Broad spectrum of location techniques proposed. Most proposals utilize a fraction of anchors with known positions. Unknowns perform physical measurements to infer location. Anchor UnknownIndoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 9
  10. 10. Measurement Techniques1. Distance related Received Signal Strength Indicator (RSSI) Time of Arrival (ToA) Time Difference of Arrival (TDoA)2. Angle of Arrival (AoA) Figure 3. Angulation based on two anchors [24]. Beamforming Phase interferometry Subspace-based3. Scene analysis RSSI-profiling (RADAR[6])4. Connectivity-based (hop-count) Figure 4. Hop-count measurement in anisotropic network.Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 10
  11. 11. Location Systems for WSNs One-hop Multihop Range-free Range-based Centralized Distributed Active Badge [1] DV-Distance [8] Active Office [2] N-hop multilateration [15] DV-Hop [8] Cricket [3] MDS range-based [12] Robust positioning [16] Amorphous [9] GPS-less [4] SDP range-based [13] Coordinate stitching [17,18] SDP [10] APIT [5] Simulated Annealing [14] Particle filters MDS [11] RSSI-profiling Kalman [19] RADAR [6] Bayesian [20,21] LANDMARK [7] Montecarlo [22,23]Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 11
  12. 12. Localization in WSNs: General trends Complexity/cost and accuracy tradeoff Selection highly dependent on specific application requirements. Accuracy Complexity Specialized HW Cost Range-based ✓ ✗ Yes ✗ Range-free ✗ ✓ No ✓ Centralized vs Distributed localization algorithms Implementation Accuracy Energy Cost complexity consumption* Centralized ✓ ✓ ✓ ↔it > hops ✗ Distributed ✗ ✗ ✓ ↔it < hops ✓ * It = Nº of iterations in distributed algorithm; hops = Avg. Nº of hops to central processing unit [25].Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 12
  13. 13. Table of Contents1. Objectives and thesis contribution2. Wireless Sensor Networks (WSNs)3. Problem statement4. State of the Art: Location Systems for WSNs5. Localization algorithms overview6. Simulation7. Experimental evaluation8. Conclusions9. Future WorkIndoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 13
  14. 14. Evaluated algorithms Common features: Truly distributed  no external infrastructure or centralized processing unit. Communication protocol based on local broadcast transmissions. Scalable to large WSNs (100+). No specialized hardware requirements. Execution divided into three stages: Phase 1: Node-to-anchor distance estimation. Phase 2: Initial node positions computation. Phase 3: Iterative refinement (optional).Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 14
  15. 15. Algorithms overview Range-based: Local Node Density-based (LND) DV-Dist RSSI-based techniques (RSSI1 and RSSI2) Range-free: DV-Hop Phase LND Algorithm A Algorithm B Algorithm C Algorithm D 1a. Range DIN DIN - RSSI-Approx1 RSSI-Approx2 1b. Distance Sum-dist DV-Dist DV-Hop RSSI-Approx1 RSSI-Approx2 1c. Distance FCH - - - - correction 2. Initial Multilateration Multilateration Multilateration Multilateration Multilateration position 3. Refinement PIV PIV PIV - -Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 15
  16. 16. LND Algorithm Phase 1a. DIN internodal range Local node density information to estimate distances Execution procedure (pair of nodes nA, nB): 1. Exchange neighbour tables 2. Determine number of nodes in union (Ku) and intersection (Ku) areas 3. Calculate area relationship H(dn) = Ai/Au 4. Yield distance estimate (normalized distance ∙ R) dAB = dn ∙ R = f(H(dn)) ∙ R nB nAIndoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 16
  17. 17. LND Algorithm Phase 1a. DIN internodal range Local node density information to estimate distances Execution procedure (pair of nodes nA, nB): 1. Exchange neighbour tables 2. Determine number of nodes in union (Ku) and intersection (Ku) areas 3. Calculate area relationship H(dn) = Ai/Au 4. Yield distance estimate (normalized distance ∙ R) dAB = dn ∙ R = f(H(dn)) ∙ R R RIndoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 17
  18. 18. LND Algorithm Phase 1a. DIN internodal range Local node density information to estimate distances Execution procedure (pair of nodes nA, nB): 1. Exchange neighbour tables 2. Determine number of nodes in union (Ku) and intersection (Ku) areas 3. Calculate area relationship H(dn) = Ai/Au ≈ Ki/Ku 4. Yield distance estimate (normalized distance ∙ R) dAB = dn ∙ R = f(H(dn)) ∙ R Ki = 4 Ku = 13 Intersection nodes+ Union nodesIndoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 18
  19. 19. LND Algorithm Phase 1a. DIN internodal range Local node density information to estimate distances Execution procedure (pair of nodes nA, nB): 1. Exchange neighbour tables 2. Determine number of nodes in union (Ku) and intersection (Ku) areas 3. Calculate area relationship H(dn) = Ai/Au ≈ Ki/Ku 4. Yield distance estimate (normalized distance ∙ R) dAB = dn ∙ R = f(H(dn)) ∙ R Ki = 4 Ku = 13 H(dn) = 4/13 Intersection nodes+ Union nodesIndoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 19
  20. 20. LND Algorithm Phase 1a. DIN internodal range Local node density information to estimate distances Execution procedure (pair of nodes nA, nB): 1. Exchange neighbour tables 2. Determine number of nodes in union (Ku) and intersection (Ku) areas 3. Calculate area relationship H(dn) = Ai/Au ≈ Ki/Ku 4. Yield distance estimate (normalized distance ∙ R) dAB = dn ∙ R = f(H(dn)) ∙ R Ki = 4 Ku = 13 dAB H(dn) = 4/13 dAB = dn ∙ R Intersection nodes 28.4 H n  92.6 H n  118.4 H n  76.5H n  27.8H n  7.5H n  1.9, ki  ku 6 5 4 3 2 + Union nodes dn   1  , ki  ku  ki  1Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 20
  21. 21. LND Algorithm Phase 1b. Initial node-to-anchor distance estimation (Sum-dist) Flood connectivity and distance data (distance-vector approach). Process initiated at anchors. Propagation control: forward packets with non-stale information. [x1,y1,0] nC nA nB nG nH nD nF nE Anchor UnknownIndoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 21
  22. 22. LND Algorithm Phase 1b. Initial node-to-anchor distance estimation (Sum-dist) Flooding procedure case scenario (1 hop) [x1,y1,1,dCA] [x1,y1,0] nC nA nB nG nH [x1,y1,1,dBA] [x1,y1,1,dDA] nD nF nE Anchor UnknownIndoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 22
  23. 23. LND Algorithm Phase 1b. Initial node-to-anchor distance estimation (Sum-dist) Flooding procedure case scenario (2 hops) [x1,y1,1,dCA] [x1,y1,0] nC nA nB [x1,y1,2,dCA+dGC] nG nH [x1,y1,1,dBA] [x1,y1,1,dDA] nD nF [x1,y1,2,dDA+dED] nE Anchor UnknownIndoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 23
  24. 24. LND Algorithm Phase 1b. Initial node-to-anchor distance estimation (Sum-dist) Flooding procedure case scenario (Complete) [x1,y1,1,dCA] [x1,y1,0] nC nA nB [x1,y1,2,dCA+dGC] nG nH [x1,y1,1,dBA] [x1,y1,1,dDA] [x1,y1,3,dCA+dGC+dHG] nD [x1,y1,3,dDA+dED+dFE] nF [x1,y1,2,dDA+dED] nE Anchor UnknownIndoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 24
  25. 25. LND Algorithm Phase 1c. Factor Correction Hop (FCH) 1. Anchors capture network propagation error in correction factors (ci). n n d j 1 r ,ij  d e,ij  j 1 ij hij hij ci    avg. error per hop, j  i n 1 n 1 2. Flood distance correction data throughout WSN. 3. Unknown corrects initial node-to-anchor distance to aj (de,ij) using cj and nº hops (hij). de ,ij  de,ij  (hij  c j )Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 25
  26. 26. LND Algorithm Phase 2. Initial node positions via multilateration Computational method to solve system of linearized equations (Ax=b). Linear equations from anchor coordinates (xi,yi) and distance estimates (di). Minimum nº of equations: n > Dim (e.g., bidimensional space n > 2). Overdetermined system  counter range error with redundancy (least squares). Simultaneous execution with Sum-dist and FCH stages (Phases 1b & 1c). Figure 5. Trilateration visualization example.Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 26
  27. 27. LND Algorithm Phase 3. Positioning Iterative Vector (PIV) refinement Increase accuracy of node position estimates in iterative manner. Local information used to recompute initial estimate: neighbour coordinates ( xit , yit ) and DIN internodal ranges ( d it ). At each iteration t+1, node updates its estimated coordinates ( xet , yet ): 1 k dit  ei t x t 1 e x   t e ( xi  xe ) t k i 0 2dit 1 k dit  ei t y t 1 e y   t e ( yi  ye ) t k i 0 2dit Correction principle: minimize mismatch between real ( ei ) and virtual ranges (estimated distance d it ). Stop condition: Fixed number of iterations. Update magnitude lower threshold δ. ( xe1  xe )  ( ye1  ye )   t t t tIndoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 27
  28. 28. LND Algorithm – PIV Example Execution 1. Exchange neighbour data 2. Update position 8 R 5 9 3 PIV refinement procedure case scenarioIndoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 28
  29. 29. LND Algorithm – PIV Example 5 (x0,y0) = (5,9) Execution 1. Exchange neighbour data 2. Update position 8 5 (xr,yr) = (3,6) 8 (2,7) 9 9 (6,5) 3 Real position(1,2) 3 Estimated position Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 29
  30. 30. LND Algorithm – PIV Example 5 (x0,y0) = (5,9) Execution 1. Exchange neighbour data 5 2. Update position (x1,y1) = (4.47,7.54) 8 1 3 dit  ei t x1  x0   t ( xi  xe )  ...  5  0.53  4.47 t 3 i  0 2d i 1 3 dit  ei t 5 (xr,yr) = (3,6) y1  y0   t ( yi  ye )  ...  9  1.45  7.55 t 8 3 i  0 2d i (2,7) 9 9 (6,5) 3 Position error iter. 0 (ξ0 = 3.6) Position error iter. 1 ( ξ1 = 2.13) Relative improvement (%) 3  0  1(1,2)  r (%)  100  40.87% 0 Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 30
  31. 31. Alternative hop-by-hop algorithms DV-Dist: Simplified version of LND (Sum-dist ≈ DV-Dist, FCH suppressed). DV-Hop: Connectivity-based distance estimation. 1. Anchors compute calibration factors (single-hop length estimation) ci   ( xi  x j )2  ( yi  y j ) 2 ,i  j  hj 2. Unknowns derive extended ranges using nº hops (hj) de,ij  hij  c j Note: Main difference: node-to-anchor distance estimation technique. Phases 2 and 3 identical to LND (Multilateration + PIV).Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 31
  32. 32. RSSI-based algorithms Range estimation: Relate RSSI and distance to sender. dist  f ( RSSI ) Preliminary study: transmission pattern analysis of ScatterWeb Modular Sensor Board (MSB)*. 40 45 40-45 50 45-50 55 60 50-55-dBm 65 55-60 70 60-65 75 65-70 80 70-75 85 75-80 5 3,75 4,5 80-85 3,75 3 2,5 m 2,25 1,5 1,25 m 0,75 0 Figure 6. Signal strength measurements from the Spectrum Analyzer. Figure 7. Spectrum Analyzer RSSI measurements. Tx power 0x01, node on lower-right corner. TX power 0x01, node on central position. Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 32
  33. 33. RSSI-based algorithms Use RSSI empirical data to yield 2 approximation range functions (MatLab): f ( x) RSSI1  0.0127 x 2  0.3697 x  2.2688 f ( x) RSSI2  0.2996 x 2  1.407 x  33.7234 *Remarks empirical RSSI analysis High spatial & temporal variability (no uniform circular model!). Chipcon CC1020 transceiver limited sensitivity (5-15dBm difference vs Spectrum analyzer). Figure 8. RSSI approximations for transmission power 0x01 indoors Tx power 0x01: higher spatial resolution. using partial mapping. Note: Phase 2 identical to LND (Multilateration). Lack Phase 3 (PIV refinement stage).Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 33
  34. 34. Table of Contents1. Objectives and thesis contribution2. Wireless Sensor Networks (WSNs)3. Problem statement4. State of the Art: Location Systems for WSNs5. Localization algorithms overview6. Simulation7. Experimental evaluation8. Conclusions9. Future WorkIndoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 34
  35. 35. Simulation environment Self-implemented C-based simulator. Simplified radio propagation: circular transmission model. Absence of propagation effects  best-case scenario. Standard scenario: L x L = 50 x 50 units square area. Grid configuration. Anchors at the edges (throughout perimeter). PIV iterations = 200. Variable network conditions: L L Transceiver communication radio (R)  R  L, R = 10 10 Number of references (A) A=4,8,16 Nº unknowns (N) 15  N  100, N =5Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 35
  36. 36. LND simulation results Phase 1a: DIN Best performance: low transmission radios. Underestimation tendency: ↑ R  ↓erel R=L/3  |Er| < 1.84m, Stdv < ± 1.3m. Figure 9. DIN ranging estimation error using 16 anchors under varying number of deployed nodes.Phase 1b: Sum-dist 2 opposite trends: Indirect paths  overshooting. Distance-vector  shortest path  undershooting. ↑ R or ↑ N  ↓erel  ↑ |Er| Best results: R < L/5  |Er| < 7.89m, Stdv < ± 4.56m. Figure 10. DIN ranging estimation error using 16 anchors under varying number of deployed nodes.Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 36
  37. 37. LND simulation results Phase 1c: FCH Tackles undershooting. Avg. improvement not ensured  robust (?) Distance mismatch reduction dependent on ability to capture propagation error  anchor placement critical. Good performance: R=3L/10, 4L/10. Most cases: ∆=4.76-73.55%.Phase 2: Multilateration Sensitive to transmission range, insensitive to anchor fraction. Error peaks  insatisfactory FCH behaviour in given topology. a) Multilateration Best performance: low-medium communication radios. L/10 < R < L/2  Er < 5m (<42.69%), Stdv < ± 2.7m Why? R < L/2 most accurate DIN ranges  best NTA distances!Phase 3: PIV refinement Performance highly dependent on DIN ranges accuracy. Favourable conditions: low tx radios, high anchor fraction. Most improvement: 30-40 first iterations (!). Not robust: accuracy degradation in certain topologies. b) PIV Competitive final results Figure 11. Position error before and after PIV R=3L/10, 4L/10  Er < 4.78m(22.88%) Stdv < ± 1.71m refinement phase (A=4).Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 37
  38. 38. Simulation performance comparison Algorithms: LND, DV-Dist, DV-Hop. Phase 1. Node-to-anchor distance estimation Low-medium tx. radio (R ≤ L/2): comparable results 5 ≤|Et| ≤10m. High tx radio (R > L/2): DV-Hop: best performer. Stable and predictable behaviour, slight overshooting. DV-Dist: performance degradation, dramatic undershooting (poorer DIN range estimates!). Sum-dist/FCH: in most cases counters negative bias, excessive correction in certain scenarios. a) Absolute distance error – 4A b) Relative distance error – 8A Figure 12. Node-to-anchor distance estimation error for varying node transmission radio deploying 75 unknowns.Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 38
  39. 39. Simulation performance comparison Phase 2. Initial position estimation (multilateration) Low-medium tx radios (R ≤ L/2): similar accuracies. DV-Hop usually worst performer. Range-based?  FCH generally outperforms DV-Dist High tx radios (R > L/2): DV-Dist  usually poorest results |Et| ≤ 17m. FCH  accuracy enhancement not ensured. DV-Hop  most satisfactory estimates |Et| ≤ 11m. a) 75 Nodes Phases 1+2 conclusions R ≤ L/2 accurate DIN ranges  Range-based algorithms ✓ DV-Dist vs Sum-dist/FCH  inconclusive results, captured propagation error? R ≤ L/2 DV-Hop best performer  stable, predictable. Range-based  degradation due to poor DIN estimates. b) 100 NodesFigure 13. Position error for varying node transmission radio using 4 anchors. Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 39
  40. 40. Simulation performance comparison Phase 3. PIV iterative refinement (it. 200) Equalize performance  convergence to almost identical final estimates. DV-Dist cheapest method (communication, computation)  most suitable for implementation! Final accuracy most related with quality of internodal ranges (it → ∞). Improvement ∆(%) dependent on: a. Initial avg. accuracy. b. DIN neighbour distance estimates. DV-Dist: moderate accuracy enhancements (10-40%) under most scenarios. DV-Hop: benefit constrained to low tx radios (30-55%). High radios  accuracy degradation! Sum-dist/FCH: highly variable improvement.Figure 14. PIV position error under varying node transmission radios Figure 15. PIV position improvement (%) under varying node using 8 anchors and deploying 75 unknowns. transmission radios using 4 anchors and deploying 50 unknowns. Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 40
  41. 41. Table of Contents1. Objectives and thesis contribution2. Wireless Sensor Networks (WSNs)3. Problem statement4. State of the Art: Location Systems for WSNs5. Localization algorithms overview6. Simulation7. Experimental evaluation8. Conclusions9. Future WorkIndoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 41
  42. 42. Testbed setup 8 x 9m indoor area (seminar room). Network configuration: uniform, horseshoe. Nº unknowns (N): 50, 100. Nº anchors (A): 4, 8. Node model: ScatterWeb Modular Sensor Board (MSB). Algorithms: LND, DV-Hop, DV-Dist, RSSI-based methods (RSSI1, RSSI2). a) Horseshoe configuration b) Uniform configuration Figure 16. Experimental testbed overview pictures.Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 42
  43. 43. Overview horseshoe configuration Anchor UnknownIndoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 43
  44. 44. Implementation on real WSN hardware Artificial circular transmission radio (R ≈ 3m): RSSI threshold of 33 (-42.5dBm) Chipcon CC1020 radio transceiver to tx. power 0x01 (-5dBm). Collision avoidance (DIN, Sum-dist/DV-Hop/DV-Dist, FCH, PIV): round-robin oriented communication protocol. Central control unit functionality: Experimental data retrieval. Indication of algorithm phase execution initiation. Monitoring and supervision. Algorithms (DV-Hop, DV-Dist, RSSI) execution integrated in LND communication protocol. Intermediate data & location results analysis: MatLab scripts.Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 44
  45. 45. Phase 1a. Internodal ranging (DIN, RSSI1, RSSI1) DIN noticeably more accurate (>50%) and precise than RSSI-based methods. Average range errors: DIN (|Et|=0.887-1.1338m ≈33%xR), RSSI-based (|Et|>2.14m). Slightly better results of DIN in: Isotropic configurations (2-15cm poorer in horseshoe). High node densities (N=100). Figure 17. Comparison of internodal range methods in horseshoe configuration using 8 anchors and 100 unknowns.Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 45
  46. 46. Phase 1a. Internodal ranging (DIN, RSSI1, RSSI1) DIN: experimental vs simulation  performance degradation (≈0.5m). Causes  undesireable propagation effects of wireless medium Reflections, refractions, scattering Selective fading Link asymmetries Figure 18. Detected link asymmetries during Neighbour Discovery.Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 46
  47. 47. Phase 1a. Internodal ranging (DIN, RSSI1, RSSI1) Bias analysis DIN: almost symmetric error distribution around 0, left slope extends to -5m (slight undershooting). RSSI-based: clear negative bias (RSSI2 higher undershooting than RSSI1). a) DIN b) RSSI1 c) RSSI2 Figure 19. Range error histogram in uniform configuration using 8 anchors and 100 unknowns.Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 47
  48. 48. Phase 1a. Internodal ranging (DIN, RSSI1, RSSI1) Error spatial distribution: greater at the edges of coverage area. Why? Proximity to potentially distorting elements (furniture, metallic doors, blackboards) a) DIN b) RSSI1 Figure 20. Absolute range error tridimensional representation in uniform configuration using 8 anchors and 50 unknowns.Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 48
  49. 49. Phase 1b-c. Node-to-anchor ranges (DV-Hop, DV-Dist, Sum-dist/FCH, RSSI1, RSSI1) RSSI-based ✗ Usually poorest performers. RSSI1 (2.37-2.79m), RSSI2 (2.32-2.66m). Undershooting tendency  relative error < -0.3184 x dr. Hop-by-hop alternatives  >0.5m more accurate, ±20-30cm more precise. DV-Hop Worst non RSSI-based alternative. Inaccuracy 0.2-0.5m higher than DV-Dist or FCH. Overshooting effect  relative error ≥ 0.0184 x dr. Cause: short routes (diameter 4-5 hops). DV-Dist ✓✓ Usually best performer despite lack of correction stage. Accuracy: 1.46-2.05m. Overestimation 0.35-0.5 x dr. Sum-dist/FCH (LND algorithm) ✓ Second best behind simplest range-based alternative DV-Dist. Accuracy: 1.59-2.66m. Generally fails to reduce initial overshooting  degradation.Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 49
  50. 50. Phase 1b-c. NTA range error per node a) DV-Hop b) DV-Dist c) FCH d) RSSI1 Figure 21. Relative node-to-anchor distance error in uniform configuration using 4 anchors and 50 unknowns.Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 50
  51. 51. Phase 1b-c. Range error distribution a) DV-Hop b) DV-Dist c) FCH d) RSSI1 Figure 22. Spatial distribution of node-to-anchor distance error in uniform configuration using 8 anchors and 100 unknowns.Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 51
  52. 52. Phase 2. Initial node positionsHop-by-hop algorithms DV-Hop ✗ Poorest performer. Highest misplacement 2.41-3.52m and imprecision ±1.04-1.57m. DV-Dist ✓✓ Usually best performer despite being cheapest/simplest alternative. Accuracy: 1.87-2.63m. Sum-dist/FCH (LND algorithm) ✓ Second best in most scenarios. Benefit of running FCH stage questionable!RSSI-based Comparable accuracies to hop-by-hop techniques: RSSI1 (2.37-2.79m), RSSI2 (2.24-2.63m). Better precision! ≤ ±0.98m (vs hop-by-hop ≤ ±1.55m). General trends Anisotropic topologies  slight performance degradation. Anchor fraction(A), node density(N)  inconclusive results.Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 52
  53. 53. Phase 2. Simulation vs Experimental Uniform: pronounced performance gap (1-3m). Horseshoe: nodes at edges benefit from transmission irregularities in real environments. a) DV-Hop b) DV-Dist Figure 23. Comparison of position errors per node in simulation and testbed environment in horseshoe configuration using 4 anchors and 100 unknowns.Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 53
  54. 54. Phase 2. Correlation NTA inaccuracy – node misplacement a) DV-Hop b) Sum-dist/FCH Figure 24. Comparison of NTA distance error vs node position errors in uniform configuration using 4 anchors and 50 unknowns.Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 54
  55. 55. Phase 2. Position error spatial distribution a) DV-Hop b) DV-Dist c) FCH d) RSSI1 Figure 25. Spatial distribution of position error in uniform configuration using 4 anchors and 100 unknowns.Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 55
  56. 56. Phase 3. PIV iterative position improvement Algorithms: DV-Hop, DV-Dist, LND. 30 iterations. 2 evaluation scenarios: High node density (N=50, 100). Low node density (N=9). Highly satisfactory performance. Most experiments: ∆DIN ≥ 10%. a) Uniform – 8A 50N Absolute accuracy improvement 0.3-1.2m. Improvement not ensured  Horseshoe 4A-100N DV-Dist (-5.45%). Variability in convergence ratio between methods (2-8%). Anchor fraction positive impact in PIV performance: ↑A  ↑↑ ∆DIN b) Horseshoe – 4A 100N Figure 26. PIV absolute accuracy improv./it.Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 56
  57. 57. Phase 3. PIV iterative position improvement Comparable improvements (%) in algorithms accross experiments. Determinant factor: initial position error. DV-Dist outperforms FCH (2-20cm better)  correction benefit questionable! DV-Dist: best final results. Accuracy 1.37-3.53m. ✓ ✓ DV-Hop: Worst performer. Lowest accuracy 1.58-3.78m and precision ±0.88-1.99m. ✗ a) Uniform – 4A 100N b) Horseshoe – 8A 50N Figure 27. PIV average position error/it.Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 57
  58. 58. Phase 3. PIV improvement per node a) DV-Hop b) DV-Dist c) FCH Figure 28. Absolute position improvement per node in horseshoe configuration using 8 anchors and 100 unknowns.Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 58
  59. 59. Phase 3. PIV improvement spatial distribution a) DV-Hop – Initial pos. error b) DV-Hop – PIV pos. improv. c) FCH – Initial pos. error d) FCH – PIV pos. improv.Figure 29. Spatial distribution of initial pos. error vs PIV pos. Improv. in uniform configuration using 8 anchors and 50 unknowns.Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 59
  60. 60. Table of Contents1. Objectives and thesis contribution2. Wireless Sensor Networks (WSNs)3. Problem statement4. State of the Art: Location Systems for WSNs5. Localization algorithms overview6. Simulation7. Experimental evaluation8. Conclusions9. Future WorkIndoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 60
  61. 61. Conclusions Simulation: No best performer in all scenarios: selection dependent on network conditions (communication range, anchor fraction, topology, node density). LND algorithm: positive results for low transmission radios R=0.3-0.4L. Absolute position error ≤ 3.943m, standard deviation ≤ ±1.71m. Experimental study: First step to bridge gap between simulations and real-world positioning systems. Internodal ranging: DIN >50% more accurate than RSSI-based methods (≤33%R). Range-based hop-by-hop methods outperform range-free counterpart (DV-Hop). RSSI-based alternatives comparable initial positions despite signal strength variability. Benefit of running additional FCH correction stage questionable. PIV highly satisfactory performance for low and medium-high node densities (∆DIN ≥ 10%, Absolute improvement 0.3-1.2m). LND algorithm: competitive final position errors for 8 anchors 1.37-2.07m.Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 61
  62. 62. Future Work Extensive simulation over ns-2 or OMNet++ discrete event platforms. Determine optimal context factors for FCH corrective procedure. Formal analysis of PIV robustness: study network constraints to guarantee convergence to more accurate position estimates. Enhancements to original PIV implementation: Filter out adjacent nodes based on consistency indicator (e.g., nº hops to anchors). Reformulation as weighted least-squares problem, associate confidence to nodes: Check convex constraints Anchor nodes are assigned maximum confidence. More and larger testbeds over extended deployment areas (multiple rooms).Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 62
  63. 63. References[1] R. Want, A. Hopper, V. Falcao, and J. Gibbons. The active badge location system. ACM Trans. Inf. Syst., 10(1):91–102, 1992.[2] A. Ward and A. Jones. A new location technique for the active office. IEEE Personal Communications, 4:42–47, 1997.[3] N. B. Priyantha, A. Chakraborty, and H. Balakrishnan. The cricket location-support system. In MobiCom ’00: Proceedings of the 6th annual international conference on Mobile computing and networking, pages 32–43. ACM, 2000.[4] N. Bulusu, J. Heidemann, and D. Estrin. Gps-less low cost outdoor localization for very small devices. IEEE Personal Communications Magazine, 7(5):28–34, October 2000.[5] T. He, C. Huang, B. M. Blum, J. A. Stankovic, and T. Abdelzaher. Range-free localization schemes for large scale sensor networks. In MobiCom ’03: Proceedings of the 9th annual international conference on Mobile computing and networking, pages 81–95, 2003.[6] P. Bahl and V. N. Padmanabhan. Radar: an in-building rf-based user location and tracking system. In INFOCOM 2000. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE, volume 2, pages 775–784, 2000.[7] L. Ni, Y. Liu, Y. Lau, and A. Patil. Landmarc: indoor location sensing using active rfid. Wirel. Netw., 10(6):701–710, 2004.[8] D. Niculescu and B. Nath. Ad Hoc Positioning System (APS). In IEEE GLOBECOM, volume 5, pages 2926–2931, 2001.[9] R. Nagpal, H. Shrobe, and J. Bachrach. Organizing a global coordinate system from local information on an ad hoc sensor network. 2nd International Workshop on Information Processing in Sensor Networks (IPSN), April 2003.[10] L. Doherty, K. Pister, and L. Ghaoui. Convex position estimation in wireless sensor networks. In Proceedings of INFOCOM 2001, volume 3, pages 1655–1663, 2001.[11] Y. Shang, W. Ruml, Y. Zhang, and M. Fromherz. Localization from connectivity in sensor networks. IEEE Transactions on Parallel and Distributed Systems, 15(11):961–974, 2004.Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 63
  64. 64. References[12] X. Ji and H. Zha. Multidimensional scaling based sensor positioning algorithms in wireless sensor networks. In SenSys ’03: Proceedings of the 1st international conference on Embedded networked sensor systems, pages 328–329, 2003.[13] P. Biswas and Y. Ye. Semidefinite programming for ad hoc wireless sensor network localization. In IPSN ’04: Proceedings of the 3rd international symposium on Information processing in sensor networks, pages 46–54, 2004.[14] A. A. Kannan, G. Mao, and B. Vucetic. Simulated annealing based localization in wireless sensor networks. The 30th IEEE Conference on Local Computer Networks, pages 513–514, 2005.[15] A. Savvides, H. Park, and M. B. Srivastava. The bits and flops of the n-hop multilateration primitive for node localization problems. In WSNA ’02: Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications, pages 112–121. ACM, 2002.[16] C. Savarese, J. Rabaey, and K. Langendoen. Robust positioning algorithms for distributed ad-hoc wireless sensor networks. In USENIX Technical Annual Conference, pages 317–327, 2002.[17] S. Capkun, M. Hamdi, and J. Hubaux. Gps-free positioning in mobile ad-hoc networks. In HICSS ’01: Proceedings of the 34th Annual Hawaii International Conference on System Sciences ( HICSS-34)-Volume 9, page 9008, Washington, DC, USA, 2001. IEEE Computer Society.[18] D. Moore, J. Leonard, D. Rus, and S. Teller. Robust distributed network localization with noisy range measurements. In SenSys ’04: Proceedings of the 2nd international conference on Embedded networked sensor systems, pages 50–61, New York, NY, USA, 2004. ACM.[19] K. Sreenath, Frank L. Lewis, and Dan O. Popa. Simultaneous adaptive localization of a wireless sensor network. SIGMOBILE Mob. Comput. Commun. Rev., 11(2):14–28, 2007.[20] V. Fox, J. Hightower, L. Lin, D. Schulz, and G. Borriello. Bayesian filtering for location estimation. IEEE Pervasive Computing, 2(3):24–33, 2003.Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 64
  65. 65. References[21] X. Sheng, Yu-Hen Hu, and P. Ramanathan. Distributed particle filter with gmm approximation for multiple targets localization and tracking in wireless sensor network. In IPSN ’05: Proceedings of the 4th international symposium on Information processing in sensor networks, page 24, 2005.[22] L. Hu and D. Evans. Localization for mobile sensor networks. In MobiCom ’04: Proceedings of the 10th annual international conference on Mobile computing and networking, pages 45–57, 2004.[23] M. Coates. Distributed particle filters for sensor networks. In IPSN ’04: Proceedings of the 3rd international symposium on Information processing in sensor networks, pages 99–107, 2004.[24] H. Karl and A. Willig. Protocols and Architectures for Wireless Sensor Networks. John Wiley & Sons, 2005.[25] M. Rabbat and R. Nowak. Distributed optimization in sensor networks. In IPSN ’04: Proceedings of the 3rd international symposium on Information processing in sensor networks, pages 20–27, 2004.Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 65
  66. 66. Thank you for your attention.Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 66
  67. 67. Questions.Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 67
  68. 68. Additional supporting slides.Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 68
  69. 69. ScatterWeb Modular Sensor Board Table 1. Key features of the ScatterWeb Modular Sensor Board (MSB-430).Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 69
  70. 70. Empirical analysis of FCH effectivity Figure 30. Analysis of FCH correction procedure effectivity . Horseshoe configuration using 8 anchors and 50 unknowns.Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 70
  71. 71. Analysis of DV-Hop effectivity Figure 31. Analysis of DV-Hop calibration effectivity . Uniform configuration using 8 anchors and 100 unknowns.Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 71
  72. 72. Extended ranges – Hop-by-hop methods a) Absolute error b) Relative error Figure 32. Comparison of NTA distance error per anchor in horseshoe configuration using 8 anchors and 100 unknowns.Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 72
  73. 73. LND algorithm power cost Estimates dependent on: Network connectivity c (avg. Neighbours/node). Nº deployed anchors a. Nº iterations executed in PIV algorithm it. Nº iterations executed for square root calculation n (Babylonian numerical method). Power cost of single transmission(Ctx) or reception(Crx) of broadcast packet (transceiver-specific). Power cost of single execution flop F (microcontroller specific). Nº dimensions of coordinates systems Dim. Table 2. Communication costs of the LND localization algorithm.Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 73
  74. 74. LND algorithm power cost Table 3. Computational costs of the LND localization algorithm. Table 4. Computational costs of the LND localization algorithm in bidimensional space (Dim = 2).Indoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 74
  75. 75. LND algorithm power cost CC1020 current consumption (868MHz transmit/receive mode) Single broadcast packet transmission P=0x01 (-5dBm) Ctx = 17.0mA Single broadcast packet reception Crx = 19.9mAIndoor Localization using Local Node Density in Ad-Hoc Wireless Sensor Networks Joaquín González Guerrero 75

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