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Finding MiMo : Tracing a Missing Mobile Phone using Daily Observations

This paper presents Finding MiMo, a system designed to trace missing mobile devices using daily observations. The approach addresses challenges in indoor tracking and relies on ambient features and movement detection without needing extensive pre-learned databases. By utilizing GPS, GSM, and WiFi signals, the system constructs paths between known places and current unknown locations, thereby providing real-time tracking and chasing capabilities. The proposed solution overcomes limitations of existing methods while minimizing energy consumption and improving accuracy, resulting in an effective tool for locating lost mobile devices.

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Finding MiMo : Tracing a Missing Mobile Phone using Daily Observations

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  1. Finding MiMo: Tracing a Missing Mobile Phone using Daily Observations Hyojeong Shin, Yohan Chon, Kwanghyo Park and Hojung Cha MobiSys2011 - Sowhat2012.03.19

  2. Outline • Introduction • System Design • Evaluation • Discussion • Conclusion

  3. Outline • Introduction • System Design • Evaluation • Discussion • Conclusion

  4. Motivation • Missing handheld device problem • Existing solution • MobileMe, provided by Apple Inc. and Skyhook • GPS, cell tower ID, WiFi fingerprints • Drawbacks - Inaccurate location estimations in indoor • GPS signal not reachable • Pre-learned database for radio fingerprints not available

  5. Challenges • Efficient - limited battery duration • Indoor environment challenge • GPS, floor plan, pre-learned radio map, non-standard hardware not generally available • Room-level accuracy • Not necessary with additional hardware functionality or infrastructure

  6. Outline • Introduction • System Design • Evaluation • Discussion • Conclusion

  7. Idea of FindingMiMo ~ Warn / Cold Game ~ Sim() = Tanimoto coefficient function =

  8. Place-matching Problem • Input • Initial known position • Logged observations • Live observations • Output • Moving path of missing mobile • Moving path of the chaser

  9. System Architecture

  10. LifeMap • Functionality • Monitor user’s physical location with ambient features • Construct nodes (places) & edges (paths) • Sensing • GPS, GSM, WiFi • Activity-based sensor selection (move / stationary) • Detection of movement – Tanimoto Coefficient > threshold • Known place • Stationary state continuously maintained • Role • Provide information of movement and known places

  11. MissingMobile:Daily Ambient Logger • Role • Record ambient radio logs on the path, from last known place to current unknown place • Functionality Log WiFi vectors , LifeMap detect movement & no GPS signal Sleep state , otherwise • Minimize stored information • Collection period < 1 Day • Reset when 1. revisit a known place 2. GPS signal available

  12. SmartSLAM:Indoor Pedestrian Tracking • Role • Provide indoor floor plan & location of the user • Functionality– floor plan construction • Path • Accelermeter step count • Digital compass  heading direction • Location • Use WiFi observation to identify locations • Adjust path while revisting identified locations

  13. Chaser: Device TrackingChasing Information • Circumstantial evidence • Approximate missing time & previous known place • Indoor pedestrian tracking • Display map & location of the user • Log similarity • Similarity of current and logged observations • Trace similarity – • Target similarity – • Chasing progress -

  14. Chaser: Device TrackingChasing Information If no specific location (GPS) in log data Visit all possible entrances of last placeto find the one with largest similarity Indoor Chasing

  15. Outline • Introduction • System Design • Evaluation • Discussion • Conclusion

  16. Missing Mobile • Energy consumption & space complexity • Setting • Similarity threshold = 0.7 • 5 students collect 2 weeks • Energy consumption

  17. Missing Mobile • Space complexity • AP – 367 ~ 1573 • Record – 50160~248700 • 4.5 ~ 22.3MB • Reduction: ABCBD  ABD

  18. Chasing a hidden device

  19. Chasing a hidden device

  20. Vertical Localization

  21. Hide-and-Seek Game • Goal: check if chaser could chase with only the information from chaser app. • Setting • 4 games at 4 different buildings • 36 people • 1 participant hide, chaser group chase

  22. Case Study: Shopping mall • Real environment & case • Setting • 195000 m2 shopping mall • 3 hours log • Extract 6 possible missing place  generate logs • User chase at the next day

  23. Outline • Introduction • System Design • Evaluation • Discussion • Conclusion

  24. Discussion • User guide • Move slow while chasing • Places with similar radio signal may be on different floor • Erroneous data • GPS may be read via window • Open space

  25. Outline • Introduction • System Design • Evaluation • Discussion • Conclusion

  26. Conclusion • FindingMiMo with ambient observations to help to trace missing mobile device • Not clear descriptions for detail settings

  27. Thanks for Listening ~

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