Opensense2

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

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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   

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Coarse models (mesoscale = 1km2)

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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

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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

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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

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• 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

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Open source, free on Android/iOS

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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