A UTO W ITNESS : L OCATING AND T RACKING S TOLEN P ROPERTY W HILE T OLERATING GPS AND R ADIO O UTAGES Santanu Guha, Kurt Plarre, Daniel Lissner, Somnath Mitra, Bhagavathy Krishna, Prabal Dutta, Santosh Kumar ACM SenSys Sowhat

O UTLINES Motivation Objectives & Challenges Related Works System Overview Hardware Design Theft detection Estimating distance & Turns Evaluation Conclusion

O UTLINES Motivation Objectives & Challenges Related Works System Overview Hardware Design Theft detection Estimating distance & Turns Evaluation Conclusion

Property crimes 17.2 billion Burglary Burglary, 22.7% $2079 / $4.6 billion 90% un-recovered Larceny-theft Larceny-theft, 67.5% $925 / $6.1 billion 90% un-recovered Motor vehicle theft Motor vehicle theft, 9.8% $6751 / $6.4 billion 80% recovered M OTIVATION According to the FBI Uniform Crime Report, 2008 an estimated losses resulted from…

O UTLINES Motivation Objectives & Challenges Related Works System Overview Hardware Design Theft detection Estimating distance & Turns Evaluation Conclusion

O BJECTIVES & C HALLENGES Objectives Detection of theft Tracking of the stolen tag Pinpointing of the location Challenges - Cost & Energy Hardware selection Theft classifier Tracking method

O UTLINES Motivation Objectives & Challenges Related Works System Overview Hardware Design Theft detection Estimating distance & Turns Evaluation Conclusion

R ELATED W ORKS Traditional home security systems Deter or detect burglary only Unable to track or recover Traditional asset tracking and vehicle recovery systems detected Not tolerate in-transit cloaking High power LoJack, the most common stolen vehicle recovery systems A device to receive the activation signal needed High-power transmissions once actived Cost, $695

O UTLINES Motivation Objectives & Challenges Related Works System Overview Hardware Design Theft detection Estimating distance & Turns Evaluation Conclusion

S YSTEM O VERVIEW

Emded tag Dormant Motion Vehicular movement Distance / turn estimate (Accelerometer / Gyro) Transfer to server (GSM / GPRS) Path reconstruction Motion detection (vibration dosimeter) Motion detection (vibration dosimeter) Sufficient time & RF power available Classification (Accelerometer) Classification (Accelerometer)

S YSTEM O VERVIEW

O UTLINES Motivation Objectives & Challenges Related Works System Overview Hardware Design Theft detection Estimating distance & Turns Evaluation Conclusion

H ARDWARE Vibration dosimeter – Motion detect 3-axis accelerometer – Theft classification & Distance estimation 3-asix gyroscope – Turns estimation GSM/GPRS modem – Transfer Epic Core platform

O UTLINES Motivation Objectives & Challenges Related Works System Overview Hardware Design Theft detection Estimating distance & Turns Evaluation Conclusion

D ESIGN – T HEFT D ETECTION F LOW D IAGRAM Deep sleep Accelerometer sampling Accelerometer sampling 4.2 Variance > threshold Compute variance of medians of every 15 samples Compute variance of medians of every 15 samples Partition 5.25 sec worth of data in 5 intervals Decision tree classifier applied Partition 5.25 sec worth of data in 5 intervals Decision tree classifier applied Majority rule  vehicular movement TrackingTracking N N Y Y

Collecting data in different scenarios Walking person car trolleys Rolling chair Activity classification work Using mobile phones to determine transportation modes[23] Feature – energy & standard deviation D ESIGN – T HEFT D ETECTION C LASSIFIER

Design - Estimating distance & Turns Angle estimate Gyro  rotation speed  single integration  change in the attitude Absolute value of 1 st difference Thresholds, D h & D l Activation time

D ESIGN - E STIMATING DISTANCE & T URNS D ISTANCE ESTIMATE 2 nd order Butterworth Filter to remove noise Median of 20 samples  mean of 10 such medians Accelerometer  Double integration  Distance Curved roads – angular rotation info. From gyro

O UTLINES Motivation Objectives & Challenges Related Works System Overview Hardware Design Theft detection Estimating distance & Turns Evaluation Conclusion

E VALUATION – T HEFT D ETECTION samples (210 acc. readings/sample) vehicle vs. person

E VALUATION – T URN E STIMATION 120 turns for 6 values of angles Ground truth – GPS on Android G1 phone

E VALUATION – D ISTANCE E STIMATION 300 different road segments (0.2 ~ 1.5miles) Ground truth – GPS on Android G1 phone

O UTLINES Motivation Objectives & Challenges Related Works System Overview Hardware Design Theft detection Estimating distance & Turns Evaluation Conclusion

C ONCLUSION Design & evaluation of the AutoWitness system Deter, detect, and track personal property theft Low-cost Ultra-low energy ItemPerformance Theft detectionNo false negative / 1.45% false positive Turn estimationPercentage error = 6.92 in average Distance estimation>95% percentage of error < 10

T HANKS FOR L ISTENING ~