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Challenges: Device-free Passive Localization for Wireless E nvironments

Challenges: Device-free Passive Localization for Wireless E nvironments. Moustafa Youssef, Matthew Mah, Ashok Agrawala University of Maryland College Park MobiCom’07. Motivation. Conventional location detection techs Must carry “tracking” objects to localization

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Challenges: Device-free Passive Localization for Wireless E nvironments

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  1. Challenges:Device-free Passive Localization for Wireless Environments Moustafa Youssef, Matthew Mah, Ashok Agrawala University of Maryland College Park MobiCom’07

  2. Motivation • Conventional location detection techs • Must carry “tracking” objects to localization • Examples) GPS, infrared, ultrasonic, radio frequency • Device-free passive (DfP) localization? • RF signal is affected by changes in the environments • Off-the-shelf technology: e.g., WiFi • Design goals • Detection • Tracking (mobility) • Identification (e.g., type, identify, size, shape, etc)

  3. Feasibility Study Scenario: a person enters a room, makes four movements with 60s pause in between movements, and then exits.

  4. Detection • Moving average based detection • Compare the difference between short-term and long-term behavior • Discretize time and calculate the moving average • Control unit time size to capture short/long-term behavior • Event detected if the relative difference between two averages exceed a threshold • Moving variance based detection • Instead of average, use variance for detection • Find the variance of RRSI samples during the entire measurement period • Event is detected if the sample deviation of a given time slot is beyond “r” times the deviation of a interference-free state

  5. Results • Moving variance to the raw data

  6. Tracking • Need to capture the relationship between signal strength and distance • Difficult due to multi-path, etc.. • Radio map building • Person move around the area w/o carrying device • Measure RSSI values and build a RSSI map • Passive Tracking • Given a map, determine the location of a mobile user • Baysian inference: • Maximize P( dist | measured RSSI ) • Map gives us P( measured RSSI | dist)

  7. Challenges • Identification function (DfP profiling) • Type, identity, mass, shape, etc. • Handling multiple entities • Automatic generation of a passive radio map • Positioning of Access Points and Monitoring Points • Other hardware and wireless techniques • Dynamic changes in the environments • Interferences (microwaves, WiFi, etc) • Privacy • Robustness

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