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OTMCL: Orientation Tracking-based Localization for Mobile Sensor Networks

OTMCL: Orientation Tracking-based Localization for Mobile Sensor Networks. Location awareness. Localization is an important component of WSNs Interpreting data from sensors requires context Location and sampling time? Protocols Security systems (e.g., wormhole attacks) Network coverage

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OTMCL: Orientation Tracking-based Localization for Mobile Sensor Networks

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  1. OTMCL: Orientation Tracking-based Localization for Mobile Sensor Networks

  2. Location awareness • Localization is an important component of WSNs • Interpreting data from sensors requires context • Location and sampling time? • Protocols • Security systems (e.g., wormhole attacks) • Network coverage • Geocasting • Location-based routing • Sensor Net applications • Environment monitoring • Event tracking • Mapping

  3. How can we determine location? • GNSS receiver (e.g., GPS, GLONASS) • Consider cost, form factor, inaccessibility, lack of line of sight • Cooperative localization algorithms • Nodes cooperate with each other • Anchor-based case: • Reference points (anchors) help other nodes estimate their positions

  4. The case of mobility in localization

  5. Our goal • We are interested in positioning low-powered, resource-constrained sensor nodes • A (reasonably) accurate positioning system for mobile networks • Low-density, arbitrarily placed anchors and regular nodes • Range-free: no special ranging hardware • Low communication and computational overhead • Adapted to the MANET model

  6. Probabilistic methods • Classic localization algorithms (DV-Hop, Centroid, APIT, etc.) compute the location directly and do not target mobility • Probabilistic approach: explicitly considers the impreciseness of location estimates • Maximum Likelihood Estimator (MLE)‏ • Maximum A Posteriori (MAP)‏ • Least Squares • Kalman Filter • Particle Filtering (Sequential Monte Carlo or SMC)‏

  7. Sequential Monte Carlo Localization • Monte Carlo Localization (MCL)‏ [Hu04] • Locations are probability distributions • Sequentially updated using Monte Carlo sampling as nodes move and anchors are discovered

  8. MCL: Initialization Node’s actual position Node’s estimate Initialization: Node has no knowledge of its location. L0 = { set of N random locations in the deployment area }

  9. MCL: Prediction Prediction: New particles based on previous estimated location and maximum velocity, vmax Node’s actual position Node’s last estimate

  10. Filtering a a Indirect Anchor Within distance (r, 2r] of anchor Direct Anchor Node is within distance r of anchor

  11. MCL : Filtering Node’s actual position Binary filtering: Samples which are not inside the communication range of anchors are discarded r Anchor Invalid samples

  12. Re-sampling • Repeat prediction and filtering until we obtain a minimum number of samples N. • Final estimate is the average of all filtered samples • If no samples found, reposition at the center of deployment area (initialization)

  13. Other SMC-based works • MCB [Baggio08] • Better prediction: smaller sampling area using neighbor coordinates • MSL [Rudhafshani07] • Better filtering: use information from non-anchor nodes after they are localized • Samples are weighted according to reliability of neighbors (non-binary filter)

  14. Problem 1: Predicted samples with wrong direction or velocity Problem 2: Previous location estimate is not well-localized Issue: Sample degradation Why don’t we tell where samples should be generated?

  15. Proposal: Orientation Tracking-based Monte Carlo Localization (OTMCL)

  16. Sensor bias • Inertial sensor is subject to bias due to • Magnetic interference • Temperature variation • Erroneous calibration • Affects velocity and orientation estimation during movement • Lower localization accuracy • No assumptions about hardware • Analyses use 3 categories of nodes for OTMCL based on β • High-precision sensors ( β= 10o) • Medium-precision sensors ( β= 30o, β= 45o) • Low-precision sensors ( β = 90o)

  17. Analysis – Convergence time relative to communication range stabilization phase ~ 7m OTMCL achieves a decent performance even when the inertial sensor is under heavy bias

  18. Analysis – Communication overhead • Reducing power consumption is a primary issue in WSNs • Limited batteries • Inhospitable scenarios • Assumes no data aggregation, compression • OTMCL needs less information to achieve similar accuracy to MSL

  19. Analysis – Anchor density OTMCL is robust even when the anchor network is sparse

  20. Analysis – Speed variance As speed increases, the larger is the sampling area  lower accuracy

  21. Analysis – Communication Irregularity OTMCL is robust to radio irregularity. Dead reckoning is responsible for maintaining accuracy

  22. Conclusion • Monte Carlo localization • Achieves accurate localization cheaply with low anchor density • Orientation data promotes higher accuracy even on adverse conditions (low density, communication errors) • Our contribution: • A positioning system with limited communication requirements, improved accuracy and robustness to communication failures • Future work • Adaptive localization (e.g., variable sampling rate, variable sample number) • Feasibility in a real WSN

  23. Thank you for your attention martins@mcl.iis.u-tokyo.ac.jp

  24. appendix

  25. OTMCL: Necessary number of samples Estimate error fairly stable when N > 50

  26. Analysis – Regular node density OTMCL is robust even when the anchor network is sparse

  27. Is it feasible? (On computational overhead) • Impact of sampling (trials until fill sample set)

  28. Radio model • Upper & lower bounds on signal strength • Beyond UB, all nodes are out of communication range • Within LB, every node is within the comm. range • Between LB & UB, there is (1) symmetric communication, (2) unidirectional comm., or (3) no comm. • Degree of Irregularity (DOI) ([Zhou04])

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