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Error Trends In Multihop Localization

Error Trends In Multihop Localization. Andreas Savvides Networked and Embedded Systems Lab Preliminary Presentation for IPSN’03. Multihop Ad-Hoc Localization. Many types of localization, each with different requirements To support network functions e.g geo-routing, based services

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Error Trends In Multihop Localization

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  1. Error Trends In Multihop Localization Andreas Savvides Networked and Embedded Systems Lab Preliminary Presentation for IPSN’03

  2. Multihop Ad-Hoc Localization • Many types of localization, each with different requirements • To support network functions e.g geo-routing, based services • Sensor networks in different environments (more fine grained) • No “one fits all” solution yet • Ultrasonic, acoustic, laser, radio ToF, RSS Methods… • Centralized vs. Distributed • Measurement quantities: magnetic field, distance, angles • What are the trends for large scale systems? • How well can localization work as systems scale?

  3. Problem Setup • Randomly dispersed network where some nodes know their locations • How scalable is this setup? • What is the best one can effect • Our experimental system based on small sensor nodes and ultrasonic distance measurements • Many aspects and tradeoffs need to be considered • Power consumption, computation, cost, latency, accuracy

  4. Evolving Ranging Technologies

  5. Error Trends in Multihop Networks • Sensor measurements are noisy • Additional error added before the result is computed • Broad classification of error • Channel error – transducers are not perfect, channel variations introduce significant error • Algorithmic and computation error – algorithms make a set of underlying assumptions, approximations, computation error • Setup error – associated with node configuration and network topology parameters

  6. Setup Error • Induced by measurement error BUT also reflected in the network configuration parameters • Deployment geometry • Network density • Beacon concentration • Measurement technology accuracy • Certainty in beacon locations • Study these trends in different network configurations and topologies using the CR-Bound

  7. Cramer-Rao Bound • Classical result from statistics that gives a lower bound on the error covariance matrix of an unbiased estimate • Our experiments assume that the underlying measurement error distribution is White-Gaussian • Based on our ultrasonic localization system measurements • Not always the case, but good enough to reveal some trends, useful to keep in mind during design and deployment time

  8. Scenario Setup • Scenarios designed to maintain uniform density • Generated in a radial pattern while controlling the number of nodes/unit area • Beacon nodes are placed in the outer ring

  9. Geometry Effects

  10. Geometry Effects II

  11. Geometry Effects III

  12. Geometry Effects IV

  13. Density Trends 6 neighbors per node RMS Error (m)

  14. Density Effects with Different Ranging Technologies 6 neighbors 12 neighbors RMS Error(m)

  15. Error propagation as network scales 10% beacons 6 neighbors per node

  16. Effect of Adding More Beacons RMS Error(m) 100 nodes 4 to 20 beacons

  17. How does Collaborative Multilateration Compare to the Bounds?

  18. An Ultrasonic Localization System • Why ultrasound • Low cost • Low power – around 5mW for 5m range • Unobtrusive – 40KHz • Accurate distance measurements – O(1cm) • Low latency between measurements • Shooting for 10 measurements per second

  19. RF TX Start 4ms Start Symbol Detected (start timer) RF Signal 4ms RF Reception Complete 15ms Ultrasound Signal (for max range) Ultrasonic Ranging Latency USND TX Start Ultrasound Detected Transmitter Receiver

  20. Ranging Characterization • Lab characterization of ranging module, at 25 pulses (temperature 21.4 Celcius)

  21. Smart Beacon Calibration

  22. Smart Beacon Calibration

  23. Smart Beacon Calibration

  24. Smart Beacon Calibration

  25. Host PC Controller Software 3D GUI Client(s) • Manager • Packet based • One to many switching • Facilitate online • processing of incoming • data • Allows direct use of • MATLAB code TCP Server Other SW Localizer Calibration SW Gateway Node Serial I/O

  26. Conclusions • For long distances RF ToF will most probably prevail… • Multihop localization are promising in many setups • Ultrasound measurement system works well for our purposes • Use network level algorithms to improve positioning performance • Do not have any comparison with relative localization schemes yet…

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