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Energy-Efficient Positioning for Smartphones using Cell-ID Sequence Matching

Energy-Efficient Positioning for Smartphones using Cell-ID Sequence Matching. (CAPS) Dario Aranguiz and Milan Dasgupta. Zero-Sum Game. Celltower localization (see Accuracy Characterization of Cell Tower Localization) Low power Low resolution

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Energy-Efficient Positioning for Smartphones using Cell-ID Sequence Matching

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  1. Energy-Efficient Positioning for Smartphones using Cell-ID Sequence Matching (CAPS) Dario Aranguiz and Milan Dasgupta

  2. Zero-Sum Game • Celltower localization (see Accuracy Characterization of Cell Tower Localization) • Low power • Low resolution • Requires knowledge of the celltower location • GPS • High power • Higher resolution • Duty-cycled GPS • Notably less power than 100% GPS • Lower resolution • Undesired consequences

  3. GPS vs. Celltower Localization

  4. Is there a better way?

  5. Motivation for CAPS • Users are predictable • Spatially • Temporally • Users change cell towers approximately every 500 meters • ~One to two minutes during a commute • Users’ locations can be [reliably] interpolated

  6. Cell-ID Transitions Illustrated

  7. CAPS – General Approach • Builds a database of routes using • Spatial history • Temporal history • Matches Cell-ID sequences to sequences • Learns on the fly – no war-driving required

  8. CAPS – Sequence Database and Learning • Database stores a list of <position, timestamp> tuples • Position is absolute • Gathered by GPS • Learning is triggered when: • User sees a new Cell-IDnot in the database • User has stayed in the cell for a “long” period of time • Learning entails: • Triggering GPS • Waiting for position lock • Storing absolute position and timestamp in database

  9. CAPS – Sequence Matching • Smith-Waterman algorithm • Assigns penalties for various conditions • Gaps in sequences • Extraneous Cell-ID in sequence • Simple mismatches in sequence • Ensures that last Cell-ID in sequence matches • Searches for sequence with best “score” • Tie breakers? • Best by time of day • Longest sequence • Match current Cell-ID to head of new sequence, check up later

  10. Sequence Matching Illustrated

  11. Position Estimation

  12. Implementation

  13. Effectiveness of CAPS Learning Method? • ~7 iterations before converging to best behavior • 4% GPS usage • Median position error <75m • Intermediate behavior (iterations 1-7) • Nearly perfect localization on the first iteration • Why? • Significant drop in performance during intermediate iterations • Why?

  14. Comparison to Periodic GPS Activation

  15. Comparison to Extrapolated Periodic GPS

  16. Comparison to Skyhook

  17. Further Discussion • Currently only evaluated in high celltower-density areas • What will happen with rural deployment? • What happens if you drive during a different time of day? • What happens when your sequence database gets large?

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