2015/12/16 Participatory Urban Sensing

Participatory Urban Sensing
Ling-Jyh Chen, Academia Sinica
cclljj@iis.sinica.edu.tw
1

Dr. Ling-Jyh Chen (cclljj@iis.sinica.edu.tw) Copyright © 20152
• Research Interests: networked sensing systems,
wireless networks, network measurements, and
social computing
• Current Major Projects:
• Crowdsensing systems: crowdsourcing
environment sensing tasks to facilitate citizen
monitoring
• Spatio-temporal data management/analysis: big
data analysis with a focus on spatio-temporal data
• https://sites.google.com/site/cclljj/
Ling-Jyh Chen 
Associate Research Fellow, Academia Sinica

Participatory Sensing
• A new sensing paradigm to exploit the
sensing capabilities of volunteers to gather,
analyze, and share local knowledge of our
surroundings.
• It does not rely on dedicated sensing
infrastructures and the top-down model of
data collection.
• It is more penetrative, and encourages
participation at personal, social, and urban
levels.
3

Urban Sensing
• Sensing systems with a focus on urban areas
• Targets:
• facilities: parking space, free water
refilling, accessibility facility, etc.
• environments: CO2, CO, SO2, PM2.5,
sound, light, etc.
• The results are beneficial for urban design,
city planning, and urban management.
4

PS US PUS
Device Phone Dedicated HW
Phone or  
Open HW
Target
mobility &
location based
environments
based
environments
based
Area surroundings surroundings surroundings
Scale all scales urban urban
Deployment volunteers government volunteers
Participatory Urban Sensing
5

Our Research Strategy
• not a “yet-another” app => we are
interested in human perception data
• not a “short-lived” app => we are
interested in durable application/
service
• not a “lab-scale” app => we wanna
move to the real field!
6

P.U.S. Examples
TPE-CMS: Comfort Measurement
System for Public Buses in Taipei
MAPS: Micro Air Pollution Sensing 
LASS: Location Aware Sensing System
7
TPE-CMS
MAPS

P.U.S. Examples
8
TPE-CMS
MAPS

Q: what are people doing
on the bus?
9
Comfort does matter!!
TPE-CMS

Q: How to measure it?
10
Questionnaire/Interview Professional Instruments
Problems: cost, timeliness, and scalability
TPE-CMS

TPE-CMS
~ A Comfort Measuring System for Public Transportation Systems
in the Greater Taipei Region
11
Public Transportation SystemsParticipants
Data Mashup and Statistics
Sensing'data'
(e.g.'locations,'
acceleration,'and'time)
Authorized'data'
(e.g.'bus'trajectories'
and'vehicle'properties)
Scoring'and'ranking'results
TPE-CMS

TPE-CMS: client
12
Bus+: app for commuters
TPE-CMS

Weighted(Average
Accelera/on(Level(
(dB)
Comfort(Index(
(CI)
CI dB Description
1、2、3 Lt ≤ 93dB Not uncomfortable
4 93dB < Lt ≤ 98dB A little uncomfortable
5 98dB < Lt ≤ 103dB Uncomfortable
6 103dB < Lt Very uncomfortable
83 88 93 98 103
1 2 3 4 5 6
dB
CI
Scoring
[ISO 2631]
TPE-CMS

Trajectory Matching
14
!User!Trajectory
1
2 3
4
5
!Bus!Trajectory
1
2
3
4
1
2
3
4
5
Di = average ( , )k k
We suppose the user is on the
b-th bus, s.t. b = arg Min Di
TPE-CMS

TPE-CMS: server
15
  !4,028!buses!
  !287!routes!
  !15!agencies!
  !1!sample!per!minute
  !931!buses!
  !122!routes!
  !10!agencies!
  !1!sample!per!minute
Public Transportation SystemsParticipants
Data Mashup and Statistics
Sensing'data'
(e.g.'locations,'
acceleration,'and'time)
Authorized'data'
(e.g.'bus'trajectories'
and'vehicle'properties)
Scoring'and'ranking'results
TPE-CMS

Lots of Research Issues
• How to reduce the energy consumed by
GPS?
• How to reduce the communication cost?
• How to improve the effectiveness of
trajectory matching?
• How to recruit people to contribute?
16
TPE-CMSEnergyComm.MatchingIncentive

Event-basedTracking (EBT)
• We use g-sensor to detect “turn” events
and turn on GPS only when necessary.
• We estimate the location information
using g-sensor data when GPS is OFF.
17
EBT
Hybrid duty-cycle  
scheduling Location estimation
Event-based
dynamic duty-cycle
(DDC):  
Static duty-cycle
Uses G-sensor data
to estimate location
EnergyComm.MatchingIncentiveTPE-CMS

EBT Evaluation
• EBT is a type of Context-aware GPS
scheduling.
• EBT can achieve 90% power saving ratio
while keeping the location error below
20m.
18

Acceleration Data Reduction
• The default sample rate is 20Hz.
• The frequency can be reduced to 0.5
using Poisson sampling.
19
-100
-50
0
50
100
-100 -50 0 50 100
Comfortscore
λ = 0.1 v.s. all
regression line
-100
-50
0
50
100
-100 -50 0 50 100
Comfortscore
λ = 1 v.s. all
regression line
-100
-50
0
50
100
-100 -50 0 50 100
Comfortscore
λ = 10 v.s. all
regression line
Poisson rate 0.01 0.02 0.04 0.06 0.08 0.1 0.5 1 5 10 20
Correlation 0.586 0.730 0.852 0.885 0.896 0.930 0.986 0.989 0.992 0.993 1
Reduce 97.5% of acceleration data!!

LosslessTrajectory Compression
• We observe that redundancy is common
in ST-data, propose Inter-Frame
Coding (IFC) for lossless trajectory
compression.
20
Comparison*with*the*state/of/the/art*

Trajectory Matching Model
21

“Participatory” Sensing
• Several strategies that we have tried
• supporting New TPE buses
• rewarding badges to active participants
• adding social network component
• participating App contest
• and more
• But, inventive is still a big issue of such
systems.
22

P.U.S. Examples
23
TPE-CMS
MAPS

Micro Air Pollution Sensing
24
MAPS
2013 2014 2015
v 2.0v 1.0 v 3.0v 0.1 v 4.0
• A collaboration with environmental researchers to
evaluate heat wave vulnerability determinants in
order to formulate corresponding heat wave
adaptation strategies in Taiwan
• The goal of our subproject is to design smart Things
for air pollution measurements.

25
MAPS
Version 1.0
Arduino Uno

26
MAPS
Version 2.0
Arduino Nano

27
MAPS
temperature/
humidity
barometer
GPS
PM 2.5
(Sharp)
CO2
PM 2.5
(PPD)
cooling
fan
Version 3.0
Intel Galileo Gen 2

28
MAPS
Version 4.0
Arduino Nano
temperature/
humidity
barometer
Bluetooth
UART
CO2
PM
RTC
Realtime Display

29
MAPS
• Lessons learned
• Lots of dirty work (more than expected),
but it worths!
• Sensor calibration is a key issue, and it’s
very HARD (much harder than expected)!
• data communication is important for real-
time monitoring (and debugging).
• One small step for a EE/CS people, one
GIANT leap for environmental research!

30
MAPS
• What’s the next for Year 2016?
• Collaboration with industry and TPE
government on
• supporting LoRa on MAPS
• urban sensing (~1000 nodes)
• Collaboration with TW government on
• air quality sensing
• heat pressure measurement  
for outdoor workers

P.U.S. Examples
31
TPE-CMS
MAPS

LASS
• A sister project of MAPS
• A successful collaboration between
academic research and maker community
• Academic research: focus on core
techniques
• Maker community: focus on volunteer
recruiting and deployment
• Both: work on data analysis and share
the results
32

LASS in a nutshell
33

LASS: System Architecture
34

LASS FieldTry
35
https://lass.hackpad.com/LASS-Field-Try-1--DGcMHcZNmYq

LASS FieldTry: PM2.5
36

LASS FieldTry: PM2.5
37

LASS FieldTry
38
https://lass.hackpad.com/LASS-Field-Try-PM2.5-7xQSIilMeGU

LASS FieldTry
39
http://www.icshop.com.tw/product_info.php/products_id/20524

LASS FieldTry
40
https://lass.hackpad.com/LASS-Field-Try-1--QhzgWXt3HJd

LASS FieldTry
41
http://nrl.iis.sinica.edu.tw/LASS/show.php?device_id=FT1_035

LASS FieldTry
42

LASS FieldTry
43

LASS FieldTry
44
MAPS
(Arduino Nano)
LASS
(LinkIt One)
LASS
(Realtek Ameba)
and more…

Lessons Learned from the
PS/US/PUS Systems
• There is no free lunch!
• DEEP research starts from
DIRTY work!
• PUS has to ask RIGHT questions,
and target RIGHT communities.
• “When you want something, all
the universe conspires in helping
you to achieve it.” ~ Paulo Coelho
45
TPE-CMS
MAPS

Still MANY Issues
• Sensor calibration (after deployment)
• Low-cost/power & long-range communication
• Just-enough sensing for energy efficiency
• Security for PS/US/PUS data and systems
• (BIG) data management, processing, analysis,
mining, and visualization
• and more…
46

Summary
• We present our recent works on PS, US,
and PUS systems.
• We discuss several research issues and
our preliminary research results, as well
as lessons learned through deployment.
• There are lots of opportunities
remaining, and we look forward to
collaboration with you!
47

Acknowledgements
• Y.-T. Huang, Y.-Y. Chen, C.-L. Wu, C.-Y. Lin, T.-A.
Wang, Y.-Y. Chang, H.-C. Lee, H.-C. Wu, H.-H. Hsieh,
C.-W. Li, Dr. C.-L. Wu, Dr. W.-C. Lee (PSU), Dr. S.-C.
Lung (AS), Dr. W.-C. Peng (NCTU), and more.
• LASS@FB, MakerPro@FB, and MANY

資訊科學研究所
地址：臺北市南港區研究院路二段128號
　　　中央研究院資訊科學研究所
郵件： cclljj@iis.sinica.edu.tw
網址： http://www.iis.sinica.edu.tw/~cclljj/
陳伶志
Institute of Information Science
Associate Research Fellow
128, Section 2, Academia Road
Institute of Information Science
Academia Sinica, Nankang
Taipei 11529, Taiwan
副研究員
電話：(02)2788-3799 ext. 1702
傳真：(02)2782-4814
統編：04142327
Tel : +886-2-27883799 ext. 1702
Fax: +886-2-27824814
E-mail: cclljj@iis.sinica.edu.tw
http://www.iis.sinica.edu.tw/~cclljj/
博士 Ph.D.Ling-Jyh Chen

2015/12/16 Participatory Urban Sensing

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2015/12/16 Participatory Urban Sensing