2015/12/16 Participatory Urban Sensing

Participatory Urban Sensing

Ling-JyhChen,[email protected]

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Dr. Ling-Jyh Chen ([email protected]) Copyright © 2015

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• 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.

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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.

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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

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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!

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P.U.S. Examples

TPE-CMS: Comfort Measurement System for Public Buses in Taipei

MAPS: Micro Air Pollution Sensing

LASS: Location Aware Sensing System

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TPE-CMS

MAPS


P.U.S. Examples

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TPE-CMS

MAPS


Q: what are people doing on the bus?

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Comfort does matter!!

TPE-CMS


Q: How to measure it?

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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

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Public Transportation Systems Participants

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

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Bus+: app for commuters

TPE-CMS


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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

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!User!Trajectory�

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2 3

4

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!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

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  !4,028!buses!  !287!routes!  !15!agencies!  !1!sample!per!minute�

  !931!buses!  !122!routes!  !10!agencies!  !1!sample!per!minute�

Public Transportation Systems Participants

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?

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TPE-CMSEn

ergy

Com

m.

Mat

chin

gIn

cent

ive


Event-based Tracking (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.

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EBTHybrid duty-cycle

scheduling Location estimation

Event-based dynamic duty-cycle (DDC):

Static duty-cycle

Uses G-sensor data to estimate location

Ener

gyC

omm

.M

atch

ing

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eTPE-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.

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Ener

gyC

omm

.M

atch

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eTPE-CMS


Acceleration Data Reduction

• The default sample rate is 20Hz.

• The frequency can be reduced to 0.5 using Poisson sampling.

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-100

-50

0

50

100

-100 -50 0 50 100

Com

fort

scor

e

λ = 0.1 v.s. allregression line

-100

-50

0

50

100

-100 -50 0 50 100

Com

fort

scor

e

λ = 1 v.s. allregression line

-100

-50

0

50

100

-100 -50 0 50 100

Com

fort

scor

e

λ = 10 v.s. allregression 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!!

Ener

gyC

omm

.M

atch

ing

Ince

ntiv

eTPE-CMS


Lossless Trajectory Compression

• We observe that redundancy is common in ST-data, propose Inter-Frame Coding (IFC) for lossless trajectory compression.

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Comparison*with*the*state/of/the/art*

Ener

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omm

.M

atch

ing

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eTPE-CMS


Trajectory Matching Model

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Ener

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omm

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atch

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eTPE-CMS


“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.

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Ener

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omm

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atch

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eTPE-CMS


P.U.S. Examples

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TPE-CMS

MAPS


Micro Air Pollution Sensing

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MAPS

2013 2014 2015v 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.



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MAPS

Version 1.0 Arduino Uno



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MAPS

Version 2.0 Arduino Nano



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MAPS

temperature/humidity

barometer

GPSPM 2.5 (Sharp)

CO2

PM 2.5 (PPD)

cooling

fanVersion 3.0

Intel Galileo Gen 2



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MAPS

Version 4.0 Arduino Nano

temperature/humiditybarometer

BluetoothUART

CO2

PM

RTC

Realtime Display



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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!



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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

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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

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LASS in a nutshell

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LASS: System Architecture

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LASS Field Try

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


LASS Field Try: PM2.5

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LASS Field Try: PM2.5

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LASS Field Try

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


LASS Field Try

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


LASS Field Try

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


LASS Field Try

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


LASS Field Try

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LASS Field Try

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LASS Field Try

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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

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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…

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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!

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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


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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： [email protected]網址： http://www.iis.sinica.edu.tw/~cclljj/

陳伶志

Institute of Information Science

Associate Research Fellow

128, Section 2, Academia RoadInstitute of Information ScienceAcademia Sinica, NankangTaipei 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. 1702Fax: +886-2-27824814E-mail: [email protected]://www.iis.sinica.edu.tw/~cclljj/

博士 Ph.D.Ling-Jyh Chen

2015/12/16 Participatory Urban Sensing

Technology

Transcript of 2015/12/16 Participatory Urban Sensing