Tracking Stolen Bikes through Everyday Mobile Phones and Participatory Sensing

1

BikeTrackTracking Stolen Bikes through Everyday Mobile Phones

and Participatory Sensing

Ted Tsung-Te Lai Chun-Yi LinYa-Yunn Su

Hao-Hua Chu

National Taiwan University

2

Bikes are everywhere

Cyclists face many problems…

3

Safety (CyberBike, HotMobile10)

4

Route quality (BikeStatic, CHI10)

5

Fitness (BikeNet, SenSys07)

Sensors:-Heart rate-GPS-Accelerometer…etc

6

Bike Theft (BikeTrack)

7

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?”

8

1. Motivation2. BikeTrack System design3. Evaluation and preliminary results4. Future work5. Conclusion

Outline

9

BikeTrack overview

Bluetooth BikeUsers use phone to scan Bluetooth

Log BluetoothID/Location/Timestamp

Server for bike location query

data

10

Spec:20-meter radio range1.5-month lifetime16 USD/tag

Customization:Only broadcast beacon ID

Why Bluetooth?Available on almost every phone

Bluetooth beacon tag

11

Bluetooth tag mounting on a bike

12

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 ID Location Timestamp

13

Server implementation

• Linux + Apache + MySQL• Web interface to query bike location on

google map

Bike locations

14

1. Motivation2. BikeTrack system design3. Evaluation and preliminary results4. Future work5. Conclusion

Outline

15

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:

CS department layout

16

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

Avg. Bluetooth detections/day

• All bikes were detected• Avg. detection rate: 5.1 times/day

17

18

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

19

Bike location distribution in Taipei

20

Bike location distribution at NTU

21

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

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)

23

1. Motivation2. BikeTrack System design3. Evaluation and preliminary results4. Future work5. Conclusion

Outline

24

Formulating deployment strategy

• How to incorporate user spatial-temporal model to reduce phone overhead?

• How to incentivize participation?

25

1. Motivation2. System design3. Evaluation and preliminary results4. Future work5. Conclusion

Outline

26

• BikeTrack - A low cost participatory sensing system for bike tracking

• Preliminary result shows that BikeTrack is a promising system to locate bikes

Conclusion

27

Questions & Answers

BikeTrack:Tracking Stolen Bikes through Everyday Mobile

Phones and Participatory Sensing

Ted Tsung-te LaiChun,Yi Lin, Ya-Yunn Su, Hao-Hua Chu

National Taiwan University