OpenSense II
C r o w d s o u r c i n g H i g h - Re s o l u t i o n Air Quality Sensing Presenter: Adrian Arfire, EPFL PI: Alcherio Martinoli, EPFL Co-PIs: Karl Aberer, Boi Faltings, EPFL Andreas Krause, Lothar Thiele, ETH Z端rich Lukas Emmenegger, EMPA Murielle Bochud, University Hospital Lausanne Michael Riediker, Institute for Work and Health, Lausanne opensense.epfl.ch
OpenSense II
Importance of Air Quality
On March 25, 2014, the WHO reported: “… in 2012 around 7 million people died – one in eight of total deaths – as a result air pollution exposure. This finding more than doubles previous estimates and confirms that air pollution is now the world’s largest single environmental health risk. ” “The new estimates are not only based on more knowledge about the diseases caused by air pollution, but also upon better assessment of human exposure to air pollutants through the use of improved measurements and technology.”
http://www.who.int/mediacentre/news/releases/2014/air-pollution/en/
OpenSense II
Urban Air Pollution Air pollution in urban areas is a global concern •
affects quality of life and health
•
urban population is increasing
Air pollution is highly location-dependent •
traffic chokepoints
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urban canyons
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industrial installations
Air pollution is time-dependent •
rush hours
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weather
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industrial activities
OpenSense II
Objectives in Air Pollution Monitoring
Accurate location-dependent and real-time information on air pollution is needed Officials •
public health studies
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environmental engineers: location of pollution sources
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municipalities: creating incentives to reduce environmental footprint
Citizens
OpenSense ultra fine particle levels map in Zürich during winter months
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advice for outside activities
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assessment of long-term exposure
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pollution maps
OpenSense II
Monitoring Today
Stationary and expensive stations
Expensive mobile high fidelity equipment
Sparse sensor network (Nabel)
Personal exposure with specialized punctual studies
3E5
3
particles/cm
4E5
1E5
OpenSense II
0
1 7
:0
0
1 6
:0
0
1 5
:0
0
1 4
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1 3
:0
0
1 2
:0
0
1 1
:0
0
:0
1 0
0 9
:0
0
0 :0 0 8
Coarse models (mesoscale = 1km2)
2E5
Garage Vehicle Road Indoor
System Vision Measurement data
Crowd-Sensors, mobile sensors, monitoring stations
Model input
Terrain, Meteorology, Source strength, Background
Modeling
Lagrangian dispersion models, Data-driven methods
High resolution urban atmospheric pollution maps OpenSense II
Overview
Air Pollutio n Mappin g
Crowds ensing Data Quality Mobile Sensor Networ ks
OpenSense II
Health Impact Studies
Air Pollutio n Mappin g
Crowds ensing Data Quality Mobile Sensor Networ ks
OpenSense II
Health Impact Studies
OpenSense Deployments 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 At NABEL stations in Dübendorf & Lausanne • Stations run by EMPA • Calibration and sensor drift evaluation • Testing new sensors 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
OpenSense II
GSN for OpenSense II backend • Unified data acquisition process • Web data access/filter/download • Sensor data archiving • Sensor data search
GPRS • • • • • • • •
packet parser system logging database server GPS interpolation advanced filtering fault detection system health monitor automatic reporting
• Time series processing
GSN
@
e ann s u La
@ GSN
OpenSense II
ich Zür
Air Pollutio n Mappin g
Crowds ensing Data Quality Mobile Sensor Networ ks
OpenSense II
Health Impact Studies
Crowdsensing: Initial efforts at the end of OpenSense Zurich prototype
Lausanne prototype
Smartphone connected to ozone sensor and various application software for Android [Hasenfratz et al., Mobile Sensing 2012]
[Predic et al., PerCom’13]
OpenSense II
AirQualityEgg
Low-cost devices for home deployment (calibration tests at the NABEL station in DĂźbendorf)
Crowdsensing: NODE devices as Potential platform Variable Inc. NODE device -
Under 500 CHF(base module + two gas sensors)
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Works via Bluetooth communication with a smartphone
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Lightweight; can be mounted on bike/car or carried in pocket
Data quality testing of NODE @EMPA -
Collected CO and CO2 recordings over two days
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Reference measurements provided by EMPA
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Promising quality for CO2 while issues with CO sensors
OpenSense II
Privacy and Incentives in sensor selection
Singla et al., poster
Incentives for data gathering [Singla & Krause, HCOMP’13] -
How to valuate and negotiate access to private information of strategic agents?
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Main contribution: Privacy-aware, adaptive, truthful mechanism with monetary incentives to compensate for information shared
Case study of air quality monitoring [Singla & Krause, HCOMP’13] Simulation studies, with data collected from survey Bids($) and Sensi vity for sharingloca on
80
Bid Sensi vity
300
60
Sensi vity
Bid ($)
400
100
200
40
100
20
0
Country
State
City
Zip
Address
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40 36 32 28 24 20 16 12 8 4 0
Budget required for specified u lity
140
Random Const Truthful Greedy Seq Truthful Greedy
Seq Greedy 0
20
40
60
80
100
U lity specified
120
140
OpenSense II
160
% Change in Budget (abs)
500
Budget required
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%Change in Budget with increasingobfusca on %Lossfrom Truthfulness
120 100
%Lossfrom Privacy
80 60 40 20 0
%Gain from Adap vity 0
10
20
30
40
50
60
70
80
Obfusca on Level (in miles radius)
90
100
TinyGSN: Mobile Activity Sensing
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TinyGSN Android app –
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Tracking user location in the city
GSN principles (wrapper, virtual sensors, streamElements)
Human-as-Sensor –
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Eberle et al., poster
activity recognition, correlation with air quality levels
Field Experiments –
Goal: 24h of recording with one battery charge
Detecting activity levels: stationary, running, walking, etc
OpenSense II
Air Pollutio n Mappin g
Crowds ensing Data Quality Mobile Sensor Networ ks
OpenSense II
Health Impact Studies
Sensor Calibration Hop-by-hop GMR algorithm • Calibrates network of noisy, unstable sensors • Leverages meeting points between any pair of sensors • Based on Geometric Mean Regression (GMR) – No regression dilution problem (no calibration bias) – Low error-accumulation over multiple hops – Resistant against sensor noise
Simulated evaluation and comparison against standard methods
Evaluation on OpenSense data set obtained with Zurich deployment [O. Saukh, D. Hasenfratz, L. Thiele, Reducing MultiHop Calibration Errors in Mobile Sensor Networks, IPSN’15]
OpenSense II
ACM/IEEE IPSN’15
Impact of Mobility on Measurements passive open sampling system
active hybrid sampling system
• Electric car node used for studying impact of mobility on measurements • Testing in parallel of novel sampling systems
high traffic NABEL
low traffic
ch
An n
ua l
• On-line monitoring of system parameters
OpenSense II
N M ano ee tin Te g ra.
park
• Flexible platform for controlled experiments
Impact of Mobility on Network Coverage
hourly
daily
weekly
monthly
• Uncontrolled mobility leads to dynamically changing coverage • Data-driven probabilistic coverage of street segments of Lausanne • Modeling framework needed for filling in the gaps in space and time [Arfire et al., SenSys’15, submitted] Probability of coverage of street segments in Lausanne
OpenSense II
Peer Incentive Mechanisms for High-quality Crowdsensing Radanovic et al., poster How to ensure data quality from the Crowd? data
Peer Truth Serum
data
• • Sensor
rewards more ‘surprisingly common’ reports discourages colluding and random reporting
Payoff
data Aggregator
Participants contribute their data for a reward Collude on 1 value
Collude on 2 values
[Radanovic & Faltings, AAAI’14], [Radanovic & Faltings, AAAI’15], [Radanovic & Faltings, AAMAS’15]
OpenSense II
Air Pollutio n Mappin g
Crowds ensing Data Quality Mobile Sensor Networ ks
OpenSense II
Health Impact Studies
Data-driven Pollution Mapping
Arfire et al., poster
IDEA: Use sensor measurements in conjunction with other explanatory data to interpolate/extrapolate pollution data Street segment-based space discretization better suited than grid-based Types of models considered so far: • Log-linear regression – NABEL station & meteorological explanatory variables only • Network-based log-linear regression – explanatory variables + measurements on other segments • Probabilistic Graphical Model
[Marjovi et al., DCOSS’15, to appear]
OpenSense II
Dispersion Modeling: The GRAMM/GRAL Modeling System
Berchet et al., poster
Setup •
GRAMM (Graz Mesoscale Model): mesoscale flow simulations for city region (100 m resolution) accounting for topography & land use
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GRAL (Graz Lagrangian Model): flow and air pollutant dispersion simulations at building resolving scale (5 m) for city area forced by GRAMM meteorological data
Achievements •
Successful setup for Lausanne, including preparation of emissions and other inputs
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Hourly maps for NO2 pollution generated, good match with observations
Next steps •
Extension to other pollutants (e.g., PM)
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Setup for Zürich
OpenSense II
[Berchet et al., EGU 2015]
Air Pollutio n Mappin g
Crowds ensing Data Quality Mobile Sensor Networ ks
OpenSense II
Health Impact Studies
Integration with Parallel Health Studies Analyze association between health and pollution exposure Health data (N=6184, 2003-2009)
Exposure data Link data by GIS
SKIPOGH (N=1100, 2009-2012)
Air pollution map based on OpenSense II dispersion model
Preliminary work has shown association with blood pressure and renal function: • Air pollution and blood pressure (CoLaus & Bus Santé) •
Positive associations of pulse pressure and systolic blood pressure with short-term PM10 exposure.
[Tsai et al., Journal of Hypertension, 2015] • Air pollution and renal function and related phenotypes (CoLaus & SKIPOGH) •
Association of increased PM10 levels with increased levels of selected urinary protein among women
OpenSense II
Pilot Study on Physical Activity vs. Air Pollution Exposure Tsai et al., poster Design of a pilot study about physical activity on exposure to air pollution
Report on recommendation
Once the pilot study is complete, we will send the volunteers recommendation reports
OpenSense II
“hRouting” - Health-Optimal Route Planner
Saukh et al., poster
>350 downloads so far
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Received much attention in local media
An n
•
ch
Open source, free on Android/iOS
N M ano ee tin Te g ra.
•
ua l
Uses UFP pollution maps developed in OpenSense to compute healthy routes for pedestrians and cyclists in Zurich
[D. Hasenfratz, T. Arn, I. de Concini, O. Saukh, L. Thiele, Demo-Abstract: Health-Optimal Routing in Urban Areas, IPSN’15]
OpenSense II
Conclusions •
OpenSense II seeks to integrate different air quality sensing platforms, including vehicular sensing networks, traditional monitoring stations, and novel crowdsensing platforms
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A solution for a viable crowdsensing platform still needs to be found.
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Simulation-based efforts for developing techniques for managing crowdsensing have seen significant progress.
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Data-quality is critical when considering mobile measurements; more so if the measurements are crowdsourced.
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Deriving accurate high resolution maps is critical for assessing correlation between population health and pollutant exposure.
OpenSense II
OpenSense II Team 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 student Murielle 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 • Nancy Hopf, senior researcher • Guillaume Suarez, postdoc • Nicole Charrière, technical staff Lothar Thiele, ETHZ-TIK, Co-PI • David Hasenfratz, PhD student • Balz Maag, PhD student • Olga Saukh, postdoc
OpenSense II
Backup Slides
OpenSense II
OpenSense Sensing Platforms •
measure gas-phase pollutants (CO, NO2, O3, CO2) and particulate matter (PM).
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Gases: mix of small, relatively low-cost, electrochemical, metal oxide, and NDIR sensors. slow response time; need re-calibration
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Particles: physical metrics: particle count and lung-deposited surface area (LDSA) nanoscale sensitivity (<100 nanometers) high cost
Chemical sensors used in the OpenSense stations
DiSCmini (left) used in Zurich and Naneos Partector (right) used in Lausanne for PM measurements
OpenSense II
View inside Zurich OpenSense platform
Exploded view of Lausanne air sampling module
Maintenance and Upgrade Examples in OpenSense II Lausanne deployment â&#x20AC;&#x201C; Upgraded mask design for solving O3 sensor corrosion problem; improves long-term stability of this sensory modality Lausanne deployment - Upgraded flow pre-processing and humidity control for Partector devices (UFP detectors); improves long term stability and effective operational duty cycle of the instrument
Zurich deployment â&#x20AC;&#x201C; EMPA outsourced the development of new, high-quality nodes for static operation to DecentLab GmbH (spin-off of EMPA); the nodes measures NO2, O3, T and H and aim at increasing the spatial density of calibration points for mobile nodes in the city of Zurich
OpenSense II
GSN for OpenSense II backend
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Additions for OpenSense deployment –
Non-unique timestamps
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Processing shortcuts
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PostgreSQL support
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New local/remote GSN communication protocol
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Same data structure as the Zürich deployment
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One Virtual Sensor per (group of) sensor(s)
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Making the join with location data
Ongoing developments: –
Monitoring / alerting (automated log processing)
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Decoupled web interface
OpenSense II