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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
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OpenSense I I
MobileSensor
Deployments
Data Quality
Crowdsensing Air PollutionMapping
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)
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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)
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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
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MobileSensor
Deployments
Data Quality
Crowdsensing Air PollutionMapping
Health ImpactStudy
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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
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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
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MobileSensor
Deployments
Data Quality
Crowdsensing Air PollutionMapping
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
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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
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
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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
Crowdsensing Air PollutionMapping
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
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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
Crowdsensing Air PollutionMapping
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