Opensense2

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OpenSense II

Crowdsourcing High-Resolution 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

A IR Q UALITY

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 A IR 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 urban canyons industrial installations

Air pollution is time-dependent •

• •

rush hours weather industrial activities OpenSense II


OBJECTIVES

IN

A IR POLLUTION MONITORING

Accurate location-dependent and real-time information on air pollution is needed Officials • •

public health studies environmental engineers: location of pollution sources municipalities: creating incentives to reduce environmental footprint

Citizens •

• •

advice for outside activities assessment of long-term exposure pollution maps

OpenSense II

OpenSense ultra fine particle levels map in Zürich during winter months


M ONITORING TODAY

Expensive mobile high fidelity equipment

Sparse sensor network (Nabel)

Stationary and expensive stations

Personal exposure with specialized punctual studies   

Garage

           

Indoor

4E5

3

2E5

OpenSense II

17 :0 0

16 :0 0

15 :0 0

14 :0 0

13 :0 0

12 :0 0

11 :0 0

10 :0 0

09 :0 0

1E5

08 :0 0

Coarse models (mesoscale = 1km2)

particles/cm

3E5

Vehicle Road


S YSTEM VISION Measurement data

Model input

Crowd-Sensors, mobile sensors, monitoring stations

Terrain, Meteorology, Source strength, Background

Modeling Lagrangian dispersion models, Data-driven methods

High resolution urban atmospheric pollution maps OpenSense II


OVERVIEW

Health Impact Studies Air Pollution Mapping

Crowdsensing

Data Quality Mobile Sensor Networks

OpenSense II


Health Impact Studies Air Pollution Mapping

Crowdsensing

Data Quality Mobile Sensor Networks

OpenSense II


OPENS ENSE D EPLOYMENTS 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

OPENS ENSE 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

OpenSense II


Health Impact Studies Air Pollution Mapping

Crowdsensing

Data Quality Mobile Sensor Networks

OpenSense II


C ROWDSENSING: I NITIAL Zurich prototype

EFFORTS AT THE END OF

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

OPENS ENSE

AirQualityEgg

Low-cost devices for home deployment (calibration tests at the NABEL station in Dübendorf)


C ROWDSENSING: NODE

DEVICES AS

POTENTIAL

Variable Inc. NODE device -

Under 500 CHF(base module + two gas sensors) Works via Bluetooth communication with a smartphone 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 Reference measurements provided by EMPA Promising quality for CO2 while issues with CO sensors

OpenSense II

PLATFORM


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? 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

Bid ($)

400

80

Bid Sensi vity

300

100

60

200

40

100

20

0

Country

State

City

Zip

Address

0

% Change in Budget with increasing obfusca on

Budget required for specified u lity 40 36 32 28 24 20 16 12 8 4 0

140

Random Const Truthful Greedy Seq Truthful Greedy

Seq Greedy 0

20

40

60

80

100

120

140

U lity specified

OpenSense II

160

% Change in Budget (abs)

Bids ($) and Sensi vity for sharing loca on

Budget required

500

Sensi vity

-

% Loss from Truthfulness

120 100

% Loss from 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


TINYGS N : M OBILE A CTIVITY S ENSING

TinyGSN Android app –

Tracking user location in the city

GSN principles (wrapper, virtual sensors, streamElements)

Human-as-Sensor –

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


Health Impact Studies Air Pollution Mapping

Crowdsensing

Data Quality Mobile Sensor Networks

OpenSense II


S ENSOR C ALIBRATION

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


IMPACT

OF

MOBILITY

ON

passive open sampling system

M EASUREMENTS 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

• Flexible platform for controlled experiments

park low traffic

• On-line monitoring of system parameters

OpenSense II


IMPACT

OF

MOBILITY

ON

NETWORK C OVERAGE

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 M ECHANISMS

FOR

H IGH-QUALITY C ROWDSENSING Radanovic et al., poster

How to ensure data quality from the Crowd? data

Peer Truth Serum • •

Sensor

rewards more ‘surprisingly common’ reports discourages colluding and random reporting

Payoff

data

data Aggregator

Participants contribute their data for a reward

Collude on Random Collude on Truthful 1 value reporting 2 values reporting

[Radanovic & Faltings, AAAI’14], [Radanovic & Faltings, AAAI’15], [Radanovic & Faltings, AAMAS’15]

OpenSense II


Health Impact Studies Air Pollution Mapping

Crowdsensing

Data Quality Mobile Sensor Networks

OpenSense II


D ATA-DRIVEN POLLUTION M APPING

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: T HE GRAMM/GRAL M ODELING SYSTEM Berchet et al., poster

Setup •

GRAMM (Graz Mesoscale Model): mesoscale flow simulations for city region (100 m resolution) accounting for topography & land use 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 Hourly maps for NO2 pollution generated, good match with observations

Next steps • •

Extension to other pollutants (e.g., PM) Setup for Zürich [Berchet et al., EGU 2015]

OpenSense II


Health Impact Studies Air Pollution Mapping

Crowdsensing

Data Quality Mobile Sensor Networks

OpenSense II


INTEGRATION

WITH

PARALLEL H EALTH STUDIES

Analyze association between health and pollution exposure Health data (N=6184, 2003-2009)

Exposure data Link data by GIS

Air pollution map based on OpenSense II dispersion model

(N=1100, 2009-2012)

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 S TUDY E XPOSURE

ON

PHYSICAL A CTIVITY

VS.

A IR POLLUTION 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” - H EALTH- OPTIMAL ROUTE PLANNER Saukh et al., poster

Uses UFP pollution maps developed in OpenSense to compute healthy routes for pedestrians and cyclists in Zurich

• • •

Open source, free on Android/iOS >350 downloads so far Received much attention in local media

[D. Hasenfratz, T. Arn, I. de Concini, O. Saukh, L. Thiele, Demo-Abstract: Health-Optimal Routing in Urban Areas, IPSN’15]

OpenSense II


C ONCLUSIONS •

OpenSense II seeks to integrate different air quality sensing platforms, including vehicular sensing networks, traditional monitoring stations, and novel crowdsensing platforms

A solution for a viable crowdsensing platform still needs to be found.

Simulation-based efforts for developing techniques for managing crowdsensing have seen significant progress.

Data-quality is critical when considering mobile measurements; more so if the measurements are crowdsourced.

Deriving accurate high resolution maps is critical for assessing correlation between population health and pollutant exposure.

OpenSense II


OPENS ENSE II T EAM 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


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