OpenSense2 - Nano-Tera 2016

8/18/2019 OpenSense2 - Nano-Tera 2016

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OpenSense I I

PI: Alcherio Martinoli, EPFLCo-PIs: Karl Aberer, Boi Faltings, EPFL

Andreas Krause, Lothar Thiele, ETH Zürich

Lukas Emmenegger, EMPA

Murielle Bochud, University Hospital LausanneMichael Riediker, Institute for Work and Health, Lausanne

NT Annual Meeting, Lausanne, April 26, 2016

OpenSense IICrowdsourcing High-Resolut ion

Air Qual i ty Sensinghttp://opensense.epfl.ch


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OpenSense I I

A I R P O L L U T I O N

Air pollution in urban areas is a global concern• affects quality of life and health

• urban population is increasing

Air pollution is highly location- and time-dependent• traffic chokepoints and rush hours

• urban canyons and weather

• industrial installations and activities

Air pollution monitoring today

• Sparse, stationary and expensive stations

• Spatial interpolation with mesoscale models (1 km2)


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OpenSense I I

O U R S Y S T E M V I S I O N

Land-use and weatherTerrain, meteorology, emission sources, population

Measurement dataCrowdsensors, mobile sensors, monitoring stations

High-resolution pollution mapsPhysics-based and data-driven modeling methods

Officials, citizensHealth studies, crowdsensing methods


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OpenSense I I

O V E R V I E W

MobileSensor

Deployments

Data Quality

Crowdsensing Air PollutionMapping

Health ImpactStudy


5/29

OpenSense I I

MobileSensor

Deployments

Data Quality


Health ImpactStudy


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OpenSense I I

D E PLO YM EN TS I N L A U SA NN E A ND Z U R I C H

10 streetcars in Zurich & 10 buses in Lausanne

•CO, NO2, O3, CO2, UFP, temperature, humidity

• Localization: GNSS for trams, GNSS fusioned

with odometry and stop information for buses

• Communication: GPRS

On top of C-Zero electric vehicle

• 100% electric, flexible mobility

• system test bed, targeted investigation tool

On top of “LuftiBus”

• Since March 2013, covers whole Switzerland

At NABEL stations in Dübendorf & Lausanne

• Stations run by EMPA• Calibration and sensor drift evaluation

• Testing new sensors

• Augmentation with dedicated static stations

in the city (e.g., DecentLab AirCubes)


7/29OpenSense I I

L A S E R - B A S E D N O 2 M E A S U R E M E NT S O N A Z U R I C H S T R E E T C A R

Collaboration with IRSense II

(instrument developed byEMPA; access to OpenSense II

network provided by TIK)


8/29OpenSense I I

GPRS

• packet parser

• system logging

• database server

•GPS interpolation

• advanced filtering

• fault detection

• system health monitor

• automatic reporting

• Unified data acquisition process

• Web data access/filter/download

• Sensor data archiving

• Sensor data search

• Time series processing

G S N F OR B A C K E N D D A T A P R O C E S S I N G

Data (mostly uncalibrated) publicly

available from both deployments


9/29OpenSense I I

MobileSensor

Deployments

Data Quality


Health ImpactStudy


10/29


11/29OpenSense I I

L E V E R A G I N G O P E NS W I S S N T S T R A T E G I C A C T I O N

• Key design choice: separate sensor front end from communication, computation,

and visualization backend and leverage smartphone capabilities

• Current progress: preliminary hardware prototype; Android middleware leveraging

TinyGSN

• On-going discussion with multiple Swiss industrial partners

Collaboration DISAL

(sensor frontend), LSIR(smartphone backend);

regular consulting and

reporting with

OpenSense II consortium

Droz et al., poster


12/29OpenSense I I

L O C A T I O N P R I V A C Y P R OTE CT IO N A ND

E X PE RI ME NT S U SI NG T I N YG S N

• Location privacy protection

• Location obfuscation algorithms

• Semantic location & attackersimulation

• Time aware location inference

•

Android privacy protectionapplication

• Activity recognition

• Localization of the user in a air-

quality map

[Guo, Calbimonte, Zhuang, and Aberer, BigData’16]

[Agir, Calbimonte, and Aberer, PrivOn’14]

Agir et al., poster


13/29OpenSense I I

MobileSensor

Deployments

Data Quality


Health ImpactStudy


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OpenSense I I

C A LI BR AT IO N O F C R O S S - S E N S I T I V E S E N S O R S

• Low-cost gas sensors

– Sensor response affected by environmentalchanges, e.g. temperature and humidity

– Cross-sensitive to multiple pollutants

• Pre-deployment testing

– Need to uncover all cross-sensitivities and

environmental dependencies

• Sensor array calibration

– Augment multiple low-cost sensors to array

– Compensates for limiting effects of low-cost

sensors

– Calibrating optimized array improves

accuracy and stability of measurements

Reference SensorReference

Sensor

array

Calibration error of NO2 measurements for

different calibration frequencies during one year

Simple sensor

calibration

Sensor array

calibration

Ordinary Least-

Squares (OLS) Multiple Least-

Squares (MLS)

[Maag, Saukh, Hasenfratz, and Thiele, EWSN’16]

Maag et al., poster


16/29

OpenSense I I

M I T I G A T I N G S L O W S E N S O R D Y N A M I C S

Wind tunnel setup

true signal

sensor model

noise

deconv. filter

estimated signal

1. Deconvolution 2. Active sniffingMethods:

Sensor response

[Arfire, Marjovi, and Martinoli, EWSN’16]

Arfire et al., poster


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OpenSense I I

M I T I G A T I N G S L O W S E N S O R D Y N A M I C S

• Parallel passive vs. active sampling

experiment

• Pump-based, raised-inlet sniffer

• More than 1h drive through Lausanne

in normal traffic

[Arfire, Marjovi, and Martinoli, AIM’16, submitted]

Arfire et al., poster


18/29

OpenSense I I

R E P U T A TI ON S Y S T EM S F O R O NL I N E

I NF OR MA TI ON F US IO N

[Radanovic and Faltings, AAMAS’16]

Participant

Pollution

model

Report

at t = 1

Report

at t = 2

Pollution map

at t = 2

R

e p u t a t i o n s y s t e m

Accepts or

discards

reports

Quality score

Contribution:

A reputation system

with provable

guarantees – limits

the influence of

malicious sensors

Simple misreporting Deceiving strategy

Our system

Avg. regret for not knowing the character of sensors

Baseline

Theory bound

(our system)

Radanovic and Faltings, poster


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OpenSense I I

S E NS OR S EL EC TI ON

Characterizing sensor accuracy

- Realistic settings relevant to OpenSense II where accuracies of the sensors are unknown

- E.g., heterogeneous and noisy sensors held by the crowd

- Which sensors to select or query next?

- Minimize monitoring while maximizing the utility of data collected

- Exploration – exploitation tradeoff

- Novel ideas based on adaptive sampling

Results and Theoretical Guarantees- Theorem 1 – Utility

Tight guarantees (lower bounds) on the utility acquired

- Theorem 2 – Sample complexity

Tight guarantees (upper bounds) on the sampling cost.

Sample

complexity

NON-ADAPTIVE ADAPTIVE-BEST ADAPTIVE-TOPL

0

200

400

600

800

1000

1200

1400

0 0.5 1 1. 5 2 2. 5

Totalnumberofqueries

V ar i ance σ2 i n the feedback val ues

x 103

ADAPTIVE-TOPL

ADAPTIVE-BEST

NON-ADAPTIVE

Unknown utilities

[Nushi et al., HCOMP’15; Singla, Tschiatschek, Krause, AAAI’16]

Singla and Krause, poster


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OpenSense I I

MobileSensor

Deployments

Data Quality


Health ImpactStudy

P B P M


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OpenSense I I

P H Y S I C S - B A S E D P O L L U T I O N M A P P I N G :T H E G R A M M / G R A L M O D E L I N G F R A M E W O R K

GRAMM

Mesoscale weather

precision model

GRAL – Disp.

Lagrangian dispersion

model

GRAL – CFD

Computational fluid

dynamics model

Complex flow aroundthe city

Accounts for topography

and land cover effects

Building-resolvingflow and turbulence

Driven by GRAMM

wind fields

Building-resolving airpollution dispersion

Driven by GRAL - CFD

[Berchet et al., PHYSMOD’15; Zink et al. PHYSMOD’15]

Zink et al., poster


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OpenSense I I

M U L T I - Y E A R S I M UL A T I O N S O F N O X A N D P M 1 0

• 5 m resolution, hourly output, 0-30 m above ground level

•Lausanne: ten years, 10 emission categories

• Zurich: two years (possible extension), 20 emission categories

• Evaluation with in situ measurements:

Bias < 10%

Correlation > 0.7 for hourly concentrations

Correlation > 0.8 for daily averages

• Data transfer to IST/CHUV partners for population exposure

Zurich, annual mean NOx

Lausanne, annual mean NOx

Comparison with NABEL NOx measurements in Lausanne

Zink et al., poster


23/29

OpenSense I I

Street segment-based space

discretization

Modeled modality: LDSA

(calibrated data from instrument)

Types of models considered so

far:

• Log-linear regression

• Network-based log-linear

regression• Probabilistic Graphical Model

• Deeply learned Artificial

Neural Network

D A T A - D R I V E N P O L L U T I O N M A P P I N G :

R E C E N T E F F O R TS F O R T H E L A U S A N N E D E P L O Y M E N T

[Marjovi et al., DCOSS’15]

[Marjovi et al., SenSys’16, submitted]

IDEA: Use sensor measurements in conjunction with other available explanatory data (land-

use, meteorology) to augment pollution data for interpolation and extension beyond bus-

network coverage


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OpenSense I I

MobileSensor

Deployments

Data Quality


Health ImpactStudy


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OpenSense I I

I N TE GR AT IO N W IT H P A R A L L E L H E A L T H S T U D I E S

Analyze association between health and pollution exposure

Air pollution maps

from OpenSense II

Health data Exposure data

Link data by GIS(N=6184, 2003-2012)

(N=1100, 2009-2012)

Note: preliminary work has shown association with blood pressure and short-term

PM10 exposure [Tsai et al., Journal of Hypertension, 2015]


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OpenSense I I

F I R S T C O R R E L A T I O N R E S U L T S ( P M 1 0 W I T H

G R A M M / G R A L M O D E L A N D C O L A U S D A T A )

-0.080

-0.060

-0.040

-0.020

0.000

0.020

0.040

0.060

0.080

1 - d a y

1 - w e e k

1 - m o n t h

1 - d a y

1 - w e e k

1 - m o n t h

3 - m o n t h

6 - m o n t h

1 - d a y

1 - w e e k

1 - m o n t h

1 - d a y

1 - w e e k

1 - m o n t h

3 - m o n t h

6 - m o n t h

1 - d a y

1 - w e e k

1 - m o n t h

1 - d a y

1 - w e e k

1 - m o n t h

3 - m o n t h

6 - m o n t h

1 - d a y

1 - w e e k

1 - m o n t h

1 - d a y

1 - w e e k

1 - m o n t h

3 - m o n t h

6 - m o n t h

NABEL Model NABEL Model NABEL Model NABEL Model

C h a n g e i n c y t o k i n e p e r 1 µ

g / m 3 i n c r e a s e i n P M 1 0

CRP IL-1β IL-6 TNF-α

Association of different exposure durations with inflammatory markers by linear mixed models, adjusting

for age, gender, BMI, smoking, alcohol, diabetes, hypertension, pressure, temperature and season.

Tsai et al., poster


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OpenSense I I

P I L O T S TU D Y O N P H Y S I C A L A C T I V I T Y V S .

A I R P O L L U T I O N E X P O S U R E

Design of a pilot study about physical activity on exposure to air pollution

Report on recommendationOnce the pilot study is complete, we will send the volunteers recommendation reports

80% of the datagathered

Tsai et al., poster


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OpenSense I I

C O N C L U S I O N S

• OpenSense II seeks to integrate different air quality sensing platforms,

including vehicular sensing networks, traditional monitoring stations, andnovel crowdsensing platforms.

• Opensense II aims at developing technology and methods leading to end-

to-end systems: sensory platforms (design, calibration, mobility), backend

data management, modeling for mapping, crowdsensing (incentive and

data quality), health impact (measurement and recommendations).

• Data quality is critical when considering mobile measurements; more so

if the measurements are crowdsourced.

• The development of an innovative crowdsensing platform insuring

reliable data quality at low cost is on-going, leveraging the NT StrategicAction OpenSWISS.

• A scientific grand challenge within the project will be to compare and

integrate physics-based with data-driven modeling methods.


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OpenSense I I

O P ENS E N S E I I T E A M

Alcherio Martinoli, EPFL-DISAL, PI

• Adrian Arfire, PhD student

•Emmanuel Droz, engineer

• Ali Marjovi, postdoc

Karl Aberer, EPFL-LSIR, Co-PI

• Berker Agir, PhD student

• Jean-Paul Calbimonte, postdoc

• Julien Eberle, PhD student

• Tian Guo, PhD student

• Mehdi Riahi, PhD studentMurielle Bochud, CHUV-IUMSP, Co-PI

• Dai-Hua Tsai, postdoc

Lukas Emmenegger, EMPA, Co-PI

• Antoine Berchet, postdoc

• Dominik Brunner, senior researcher

• Christoph Hüglin , senior researcher

• Michael Müller, postdoc

• Katrin Zink, postdoc

Boi Faltings, EPFL-LIA, Co-PI

• Goran Radanovic, PhD student

Andreas Krause, ETHZ-LAS, Co-PI

• Adish Singla, PhD student

Michael Riediker, IST, Co-PI

• Nicole Charrière, technical staff

•Nancy Hopf, senior researcher

• Guillaume Suarez, postdoc

Lothar Thiele, ETHZ-TIK, Co-PI

• David Hasenfratz, PhD

• Balz Maag, PhD student

• Olga Saukh, postdoc

• Zimu Zhou, postdoc

OpenSense2 - Nano-Tera 2016

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