Discussion of mobile devices and how mobile apps can serve as new sensors that are carried around by the general public. Includes discussion of current technology, and implications of such tracking on device battery life and data transer, including benchmark results from actual devices.

1. © USF 2010
Patents Pending
Participatory Sensing:
Smart Phones as Sensors
in a Connected World
Sean J. Barbeau, M.S.
Research Associate
Center for Urban Transportation Research
University of South Florida
National
Centerfor
Transit
Research

2. © USF 2010
Patents Pending
Opportunities
• Proliferation of cell phones
– Over 67% of the world‟s population (4.6 billion) and
93% of U.S. (292.8 million) are mobile subscribers
(Jan. 11) [1][2]
– 29% of U.S. Households are Wireless–Only (Dec. 10)
– E-911 mandate for locating cell phones
• Proliferation of cell phone “apps”
– While data is being collected from participant via
phone, location-aware mobile apps can provide
services to user (e.g. personalized traffic reports)
• Incentive for extended survey participation
• Longer survey periods with smaller samples for study
[1] International Telecommunications Union, “Measuring the Information Society - The ICT Development Index,” International
Telecommunications Union, 2009. [PDF]. Available: http://www.itu.int/ITU-D/ict/publications/idi/2009/material/IDI2009_w5.pdf. [
[2] http://www.ctia.org/media/industry_info/index.cfm/AID/10323
[3] http://news.yahoo.com/s/ap/20091216/ap_on_hi_te/us_cell_phones_only

3. © USF 2010
Patents Pending

4. © USF 2010
Patents Pending
TRAC-IT
• Mobile software for GPS-enabled
phones
– Like an iPhone App
– It‟s OPT-IN
• Features:
– Apps for low to high tier phones (Java Micro Edition,
with Google Android under development)
– Records a person‟s travel behavior (an electronic
activity diary)
– Collects O/D and route information via GPS for all
modes
– Increases quality and quantity of collected information
– Provides “hyper-personalized” real-time travel
information services (e.g., traffic alerts)

5. © USF 2010
Patents Pending
TRAC-IT
• Two modes for TRAC-IT:
– PASSIVE
• No interactions with user, runs in background
• Records GPS path, provides real-time services
– ACTIVE
• Adds questions at the
end of their trips:
– Name for location
– Mode of Transportation
– Purpose of Trip
– Occupancy of Vehicle
+
TRAC-ITTRAC-IT
<- Back Select
(1) Work Related
(2) Shopping
(3) Pickup
Someone
(4) Go Home
etc. ...
Purpose of Trip:

6. © USF 2010
Patents Pending
Assisted GPS data from TRAC-IT
Asstd. GPS data
from TRAC-IT

7. © USF 2010
Patents Pending
7
Adding Sensors to the
System Architecture
Sensing System

8. © USF 2010
Patents Pending
8
Real-time Mobile Sensors

9. © USF 2010
Patents Pending
9
Collecting Environmental
Sensor Data

10. © USF 2010
Patents Pending
10
Visualization - Website
http://tinyurl.com/46hg8o5

11. © USF 2010
Patents Pending
11
Bug Labs – Modular Hardware

12. © USF 2010
Patents Pending
12
Remote Health Sensing
• Monitoring vital signs in real time
• Heart rate, respiration rate, blood pressure, skin
temperature, ECG, tri-axial acceleration

13. © USF 2010
Patents Pending
13
Collecting Health Data

14. © USF 2010
Patents Pending
Impact of GPS on Battery
0
5
10
15
20
25
30
35
40
45
4 8 15 30 60 150 300
BatteryLife(hours)
Interval Between GPS Fixes (seconds)
Sanyo Pro 200
Sprint CDMA
EV-DO Rev. A
network
14

15. © USF 2010
Patents Pending
Solution: GPS Auto-Sleep
Sanyo Pro 200
Sprint CDMA
EV-DO Rev. A
network
0
20
40
60
80
100
120
140
160
180
200
1
21
41
61
81
101
121
141
161
181
201
221
241
261
281
301
321
341
361
381
401
421
441
461
481
501
521
541
561
581
601
621
641
661
681
701
721
741
761
781
801
821
841
861
881
901
921
941
961
981
1001
1021
TimeBetweenSequentialGPSFixes(s)
“Asleep”
“Awake”
15

16. © USF 2010
Patents Pending
Impact of Wireless on Battery
16
Asstd. GPS data
from TRAC-IT Sanyo 7050
Sprint CDMA
1xRTT
Network
UDP

17. © USF 2010
Patents Pending
Asstd. GPS
data from
TRAC-IT
Solution: Critical Point Algorithm
17

18. © USF 2010
Patents Pending
Asstd. GPS
data from
TRAC-IT
Fast GPS Clustering quickly finds
Points-of-Interest (POIs)
18
Points are clustered
based on proximity
to form a POI
The remaining unclustered points
naturally form discrete trips with
Points of Interests (i.e., clusters) as
starting and ending locations
POI 1
POI 2POI 3
Trip 1
Trip 2

19. © USF 2010
Patents Pending
Asstd. GPS
data from
TRAC-IT
Ex. Car Trip
BEFORE – Raw GPS data points
AFTER - Points-of-interest identified as spatial regions
Generates:
–Trip start/end times & locations
–Dwell times at POIs
19

20. © USF 2010
Patents Pending
Ex. Walking trip
20

21. © USF 2010
Patents Pending
Merging User POIs
• Multiple visits to the same “location”
should be registered with same POI
• Needed to count
visitation frequency
• Algorithm uses POI
overlap/bounding
boxes to merge
similar POIs
• Can be per user, or
aggregate
21

22. © USF 2010
Patents Pending
Predictive Services

23. © USF 2010
Patents Pending
Acknowledgements
• Center for Urban Transportation Research
(CUTR)
– Phil Winters, Nevine Georggi
• USF Computer Science Department
– Miguel Labrador, Rafael Perez
• National Center for Transit Research
• Florida Department of Transportation
• US Department of Transportation
• National Science Foundation
• Sprint-Nextel Application Developer Program
23

24. © USF 2010
Patents Pending
Questions?
Research Associate
Center for Urban Transportation Research
University of South Florida
(813) 974-7208
USF Location-Aware Information Systems Lab:
http://www.locationaware.usf.edu/
Sean J. Barbeau, M.S.
24

25. © USF 2010
Patents Pending
For Additional Reading…
• Paola A. Gonzalez, Jeremy S. Weinstein, Sean J. Barbeau, Miguel A. Labrador, Philip L. Winters, Nevine L.
Georggi, Rafael A. Perez. “Automating Mode Detection for Travel Behavior Analysis by Using GPS-
enabled Mobile Phones and Neural Networks,” Institution of Engineering and Technology Intelligent
Transportation Systems Journal. doi: 10.1049/iet-its.2009.0029 (to appear 2010).
• Sean J. Barbeau, Nevine L. Georggi, Philip L. Winters. “TRAC-IT: Travel Behavior Data Collection using
GPS-enabled Mobile Phones,” Human Factors 135 F – Quantifying Driving-Risk Exposure Committee
Meeting at National Academy of Sciences’ Transportation Research Board 89th Annual Meeting.
Washington, D.C., January 9th, 2010.
• Sean J. Barbeau, Miguel A. Labrador, Nevine L. Georggi, Philip L. Winters, Rafael A. Perez. “TRAC-IT: A
Software Architecture Supporting Simultaneous Travel Behavior Data Collection and Real-Time Location-
Based Services for GPS-Enabled Mobile Phones,” Proceedings of the National Academy of Sciences’
Transportation Research Board 88th Annual Meeting, Paper #09-3175. January, 2009.
• Narin Persad-Maharaj, Sean J. Barbeau, Miguel A. Labrador, Philip L. Winters, Rafael Perez, Nevine Labib
Georggi. “Real-time Travel Path Prediction using GPS-enabled Mobile Phones,” 15th World Congress on
Intelligent Transportation Systems, New York, New York, November 16-20, 2008.
• Sean J. Barbeau, Miguel A. Labrador, Philip L. Winters, Rafael Perez, Nevine Labib Georggi. “Trac-It - A
„Smart‟ User Interface For A Real-Time, Location-Aware, Multimodal Transportation Survey,” 15th World
Congress on Intelligent Transportation Systems, New York, New York, November 16-20, 2008.
• Paola A. Gonzalez, Jeremy S. Weinstein, Sean J. Barbeau, Miguel A. Labrador, Philip L. Winters, Nevine
Labib Georggi, Rafael Perez. “Automating Mode Detection Using Neural Networks and Assisted GPS Data
Collected Using GPS-Enabled Mobile Phones, 15th World Congress on Intelligent Transportation Systems,
New York, New York, November 16-20, 2008.
• Sean J. Barbeau, Miguel A. Labrador, Alfredo Perez, Philip Winters, Nevine Georggi, David Aguilar, Rafael
Perez. “Dynamic Management of Real-Time Location Data on GPS-enabled Mobile Phones,” Presented at
UBICOMM 2008 – The Second International Conference on Mobile Ubiquitous Computing, Systems,
Services, and Technologies, Valencia, Spain, September 29 – October 4, 2008. © 2008 IEEE.
http://www.locationaware.usf.edu/publications.htm