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GPS-less Low-Cost Outdoor Localization for Very Small Devices

GPS-less Low-Cost Outdoor Localization for Very Small Devices. Nirupama Bulusu, John Heidemann, and Deborah Estrin. Design Goals. RF-based Receiver-based Ad hoc Responsive Low Energy Adaptive Fidelity. In this paper …. Related Work Algorithm for Coarse-grained Localization

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GPS-less Low-Cost Outdoor Localization for Very Small Devices

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  1. GPS-less Low-Cost Outdoor Localizationfor Very Small Devices Nirupama Bulusu, John Heidemann, and Deborah Estrin

  2. Design Goals • RF-based • Receiver-based • Ad hoc • Responsive • Low Energy • Adaptive Fidelity

  3. In this paper … • Related Work • Algorithm for Coarse-grained Localization • Implementation • Results

  4. Related Work • Fine-Grained Localization • Coarse-Grained Localization

  5. Fine-Grained Localization • Range Finding • Timing • Signal Strength • Signal Pattern Matching • Directionality Based • Electrical Phasing • Small aperture Direction Finding

  6. Timing • Time of flight of communication signal • Signal Pattern • Global Positioning System • Local Positioning System • Pinpoint’s 3D-iD • Different modalities of communication • Active Bat

  7. Signal Strength • Attenuation of radio signal increases with increasing distance • RADAR • Wall Attenuation Factor based Signal Propagation Model • RF mapping

  8. Signal Pattern Matching • Multi-path phenomenon • Signature unique to given location • Data from single point sufficient • Robust • Substantial effort needed for generating signature database

  9. Fine-Grained Localization • Range Finding • Timing • Signal Strength • Signal Pattern Matching • Directionality Based • Electrical Phasing • Small aperture Direction Finding

  10. Small Aperture Direction Finding • Used in cellular networks • Requires complex antenna array • Disadvantages • Costly • Not a receiver based approach

  11. Coarse-Grained Localization • Infrared • Active Badge – fixed sensors • Fixed transmitters • Disadvantages • Scales poorly • Incurs significant installation, configuration and maintenance costs

  12. Localization Algorithm • Multiple nodes serve as Reference points • Reference points transmit periodic beacon signals containing their positions • Receiver node finds reference points in its range and localizes to the intersection of connectivity regions of these points

  13. An Idealized Radio Model • Perfect spherical radio propagation • Identical transmission range for all radios

  14. Terms • d : Distance b/w adjacent ref. points • R : Transmission range of reference point • T : Time interval between two successive beacons • t : Receiver sampling time • Nsent(i,t): No. of beacons sent by Ri in time t • Nrecv(i,t): No. of beacons sent by Ri received in t

  15. contd… • CMi : Connectivity metric for Ri • S : Sample size for connectivity metric • CMthresh : Threshold for CM • (Xest, Yest) : Estimated location of receiver • (Xa, Ya) : Actual location of receiver

  16. contd… • CMi = (Nrecv(i,t) / Nsent(i,t)) * 100 • t = (S + 1 + ε) * T , 0 < ε « 1 • k = No. of reference points within connectivity range • (Xest, Yest) = (avg(Xi1+…+Xik), avg(Yi1+…+Yik)) • LE = Sqrt( (Xest – Xa)2 + (Yest – Ya)2)

  17. Model

  18. Validation of Model 78 points measured 68 correct matches Mismatches were all at the edge Error <= 2m CMthresh = 90 R = 8.94m

  19. Results T = 2s S = 20 t = 41.9s d = 10m

  20. contd… Average error 1.83m Standard deviation 1.07m Max. error 4.12m

  21. contd…

  22. contd… Simulation to check the effect of increasing the overlap of ref. points Calculated for 10,201 points NO MONOTONIC INCREASE

  23. Discussion and Future Work • Collision Avoidance • Tuning for Energy Conservation • Non-uniform reference point placement • Reference Point Configuration • Robustness • Adaptation to Noisy Environment

  24. Questions ???

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