Tracking Stolen Bikes through Everyday Mobile Phones and Participatory Sensing Ted Tsung-Te Lai Chun-Yi Lin Ya-Yunn Su Hao-Hua Chu National Taiwan University.

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Presentation transcript:

Tracking Stolen Bikes through Everyday Mobile Phones and Participatory Sensing Ted Tsung-Te Lai Chun-Yi Lin Ya-Yunn Su Hao-Hua Chu National Taiwan University 1

Bikes are everywhere 2 Cyclists face many problems… 

Safety (CyberBike, HotMobile10) 3

Route quality (BikeStatic, CHI10) 4

Fitness (BikeNet, SenSys07) 5 Sensors: -Heart rate -GPS -Accelerometer …etc

Bike Theft (BikeTrack) 6

Bike Theft Survey (208 students) 1 out of 1.8 person has bike stolen experience 1 out of 3.7 stolen bikes was recovered Mostly found on campus “Is it possible to use participatory sensing to recover stolen bikes?” 7

1.Motivation 2.BikeTrack System design 3.Evaluation and preliminary results 4.Future work 5.Conclusion Outline 8

BikeTrack overview 9 BluetoothBikeUsers use phone to scan Bluetooth Log BluetoothID/Location/Timestamp Server for bike location query data

Spec: 20-meter radio range 1.5-month lifetime 16 USD/tag Customization: Only broadcast beacon ID Why Bluetooth? Available on almost every phone Bluetooth beacon tag 10

Bluetooth tag mounting on a bike 11

Phone implementation Android 2.1 Scan Bluetooth ID every 20secs in background When a Bluetooth ID is found, it logs Auto-upload data during network availability Bluetooth IDLocationTimestamp 12

Server implementation Linux + Apache + MySQL Web interface to query bike location on google map 13 Bike locations

1.Motivation 2.BikeTrack system design 3.Evaluation and preliminary results 4.Future work 5.Conclusion Outline 14

User study Two-week during summer 11 CS grad students Dataset: 3700 bluetooth/location/times entries – 3500 self-detection; 200 detection of other users Constraint: 15 CS department layout

1.How well does participatory sensing work in tracking bikes? 2.Is it possible to locate stolen bike on campus? 3.Is it possible to reduce battery consumption based on user behaviors ? Evaluation and preliminary results 16

Avg. Bluetooth detections/day All bikes were detected Avg. detection rate: 5.1 times/day 17

1.How well does participatory sensing work in tracking bikes? 2.Is it possible to locate stolen bike on campus? 3.Is it possible to reduce battery consumption based on user behaviors ? Evaluation and preliminary results 18

Bike location distribution in Taipei 19

Bike location distribution at NTU 20

1.How well does participatory sensing work in tracking bikes? 2.Is it possible to locate stolen bike on campus? 3.Is it possible to reduce phone battery consumption based on user behaviors ? Evaluation and preliminary results 21

Avg. user detection pattern during a day 22 Detection happened at noon, dinner, end of a day Detection pattern varies with users Future optimization (currently scan/20 seconds)

1.Motivation 2.BikeTrack System design 3.Evaluation and preliminary results 4.Future work 5.Conclusion Outline 23

Formulating deployment strategy 24 How to incorporate user spatial-temporal model to reduce phone overhead? How to incentivize participation?

1.Motivation 2.System design 3.Evaluation and preliminary results 4.Future work 5.Conclusion Outline 25

BikeTrack - A low cost participatory sensing system for bike tracking Preliminary result shows that BikeTrack is a promising system to locate bikes Conclusion 26

Questions & Answers BikeTrack: Tracking Stolen Bikes through Everyday Mobile Phones and Participatory Sensing Ted Tsung-te Lai Chun,Yi Lin, Ya-Yunn Su, Hao-Hua Chu National Taiwan University 27