1 The Effects of Ranging Noise on Multihop Localization: An Empirical Study Kamin Whitehouse Joint With: Chris Karlof, Alec Woo, Fred Jiang, David Culler IPSN ‘05 4/24/05

2 Introduction Ranging Localization Single-hop Multi-hop

3 Introduction Ranging Localization Single-hop Multi-hop “Noisy Disk”

4 Introduction Ranging Localization Single-hop Multi-hop “Noisy Disk” Unit Disk Connectivity Guassian Noise Design and comparison Optimal solutions Cramer-rao bounds Algorithmic proofs Empirical parameters Prediction gap Difference between predicted and observed error d max σ

5 Introduction Localization Error Empirical Deployment Noisy Disk Prediction Gap

6 Methodology Localization Error Empirical Deployment Noisy Disk Model BModel C Significant Dominant Sufficient

7 Outline Deployment Setup Simulation Methodology Comparisons and Analysis

8 Ultrasound Hardware Circuitry derived from the Medusa node Cricket’s RF envelope Millibots reflective cone

9 Radio (RSS) Chipcon CC1000 similar fidelity to WiFi In our experiments, 2m std error near 20m range RFIDeas: 2m std error near 2m range RFM DR3000 and TR1000: 6m std error near 6m range

10 DV-distance Algorithm True distance to anchor is approximated by shortest-path distance Representative of large class using shortest path or bounding box Zig-zag makes paths longer Noise makes paths shorter [16]

11 Ultrasound Deployment 49 nodes on a paved surface 13x13m area 4 anchor nodes Randomized grid topology Distributed implementation 7 executions Median localization error of 0.78m

12 Signal Strength Deployments 49 and 25 node topologies in a grassy field 50x50m area Median localization error ~4.3 and 13.4m Comparable to GPS

13 Outline Deployment Setup Simulation Methodology Comparisons and Analysis

14 Traditional Simulation Ranging estimates are generated using parametric functions Noisy Disk Parameters σ and d max must be estimated from data [16]

15 Parameter Estimation [14] Maximum Range: d max Error: σ

16 Statistical Sampling For each in simulation, randomly choose Data set includes ranging failures Can be divided into two components Sampled Noise Sampled Connectivity ± Ranging Failures

17 Data Collection

18 Data Collection Traditional Data Collection Low spatial resolution Single pair of nodes at a single orientation Single path through space Our Data Collection For each, ~400 empirical readings taken within 0.05m Represents wide range of node, antenna, and orientation variability Captures variability due to dips, bumps, rocks, etc

19 Outline Deployment Setup Simulation Methodology Results and Analysis

20 Experimental Setup 2 Connectivity and noise components Unit Disk connectivity (D) Gaussian noise (G) Sampled connectivity (S) Sampled noise (S) Hybrid Simulations (C/N) Unit Disk Sampled Conn No NoiseGaussian NoiseSampled Noise D/N S/N D/GD/S S/SS/G

21 Experimental Setup Unit Disk Sampled Conn No NoiseGaussian NoiseSampled Noise D/N S/N D/GD/S S/SS/G Localization Error D/N S/N D/GD/S S/SS/G Deployment

22 49 Node RSS Experiment Unit Disk Sampled Connectivity No NoiseGaussian NoiseSampled Noise D/N S/N D/GD/S S/SS/G D/ND/GD/SS/NS/SS/G Deployment D/N S/N D/NS/N

23 49 Node Ultrasound Experiment Unit Disk Sampled Connectivity No NoiseGaussian NoiseSampled Noise D/N S/N D/GD/S S/SS/G D/ND/GD/SS/NS/SS/G Deployment D/ND/GD/S D/ND/GD/S

24 Non-disk like Connectivity

25 Non-disk like Connectivity Less constraints on location Reduced connectivity can cause more “zig-zag” in the shortest paths This increases shortest-path distance [16]

26 49 Node Ultrasound Experiment Unit Disk Sampled Connectivity No NoiseGaussian NoiseSampled Noise D/N S/N D/GD/S S/SS/G D/ND/GD/SS/NS/SS/G Deployment

27 Non-Gaussian Noise

28 Non-Gaussian Noise The shortest-path algorithm selectively chooses underestimated distances Heavy-tailed noise can decrease shortest path distance [16]

29 25 Node RSS Experiment Unit Disk Sampled Connectivity No NoiseGaussian NoiseSampled Noise D/N S/N D/GD/S S/SS/G D/ND/GD/SS/NS/SS/G Deployment Significant

30 Conclusions Non-disk like connectivity Non-Gaussian noise Methodology A deployment is required to evaluate predictive ability of a model