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OpenSense
OPENSENSE
OPEN INFRASTRUCTUREFOR AIR QUALITY MONITORING
Karl Aberer, EPFL
Boi Faltings, Alcherio Martinoli, Martin Vetterli, EPFL
Lothar Thiele, ETH ZrichJan Blom, Nokia Research Center Lausanne
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OVERVIEW
Motivation and Research challenges
Research progress and results
1. Sensing System
2. Data Analysis3. User Concerns
4. End-to-end System Architecture
Conclusions
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MOTIVATION AND RESEARCH CHALLENGES
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AIR POLLUTION
Air pollution in urban areas is a global concern
affects quality of life and health
urban population is increasing
Deaths from Urban Air Pollution (courtesy CHUV)
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CITIZEN USE: NOKIA USER STUDYKey questions:
How is air quality perceivedand how does it affect thedaily life?
Method:
Probe study including diaryabout the different air qualitysituations in the familys life
Participants:Six families from
Helsinki region
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EXAMPLE FINDINGS
Understanding the limitationsof senses: what pollutants wecannot sense?
The lack of air pollution related knowledge and perceptual gaps
What are thehealth impactsof differentpollutants?
What are preventivestrategies?
Connection betweenperception and objectivetruth?
For instance, is the windcoming from seas
direction really fresh?
What pollutants areincluded in the perceptionof polluted air?
?
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DESIGN DRIVERS
Track Back Air
Care Through Air
Air Belongs to All
Relieve and DiscoverFresh Moments
My Mobile Air
Edutain Air
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Care Through Air
Parents taking care of their childrenwith the help of an air quality service
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AIR POLLUTIONAND
CARDIOVASCULAR MORTALITYHealth studies show that air pollution increases the risk of cardiovascular mortality(hearth attacks) by 5% to 20% at least
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HEALTH STUDY SCENARIO
Under discussion with University Hospital CHUV and NOKIA
Assessment of impact of air pollution an health
E.g. blood pressure, renal activity, respiration
Effects are immediate High temporal and spatial
resolution of air quality datarequired
Trajectories of study subjectsare needed
Correlation with activity
and health parameters On-body sensors Activity recognition (using
mobile phones)
User concerns Study participants are sensitive
data privacy Participants would like
personalized information aboutindividual exposure and risks
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CHALLENGESTOMEET
1. Sensing system With sufficient temporal and spatial resolution
With sufficient precision
At reasonable cost
2. Data analysis Interpolate air quality parameters from raw data
Ensure data quality
Reduce acquisition cost
3. User concerns Correlate with activity and mobility data Consider privacy concerns
Provide individualized information
4. End-to-end system architecture
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1. S ENSING SYSTEM
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MONITORING TODAY
Stationary andexpensive stations
Expensive mobilehigh fidelity equipment
Sparse sensor network(Nabel)
Coarse models
(mesoscale = 1km2)
Data difficult to integrate into applications(e.g. for correlating with other features like
peoples activities)
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OPPORTUNITIES
Wireless communication
and low cost sensors:deploy larger numbers ofstations
Mobility:deploy mobile stations toincrease spatial coverage
Communities:citizens as data producersand informationconsumers
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SENSOR MODELING Understand behavior of
electro-chemical sensors
sensor dynamics
linearity
sensitivity to humidity
variability with different flowconditions
Output[V]
Selected City Technology A3CO Sensor - measured and modeled response
Martinoli
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SAMPLING SYSTEM Slow response of chemical
sensors replacing passive sampling with
active sniffing 3D printing for fast prototyping 1st model of sniffer for CO2 sensor
faster response by more than 50%
inlet
outlet
towards miniature pump
Telaire 6613 CO2 sensor
Prototype CO2 snifferon Khepera III mobile robot
Amplitude[ppm]
Martinoli
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ON-THE-FLY CALIBRATION Challenge:
Supplied calibration may not match project requirements
Baseline drift due to sensor aging
Approach:
Initial calibration using stationary, high quality instruments
When deployed periodic recalibration using mobile sensor nodesOriginal calibrationperforms with anaverage error of
30ppb
After recalibrationthe average errordrops below 3ppb
Thiele D. Hasenfratz, O. Saukh, and L. Thiele, On-the-fly Calibration of Low-cost GasSensors, Proc. of 9th European Conference on Wireless Sensor Networks (EWSN2012), Trento, Italy, Feb. 2012.
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MULTI-HOP CALIBRATION
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PLATFORMS
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LAUSANNE DEPLOYMENT
Planned12 stationary stations
NO2, CO, Humidity,Temperature
Solar panel powered Communication: GSM,
Wireless multi-hop routing
8 mobile stations NO2, CO, CO2, Humidity, Temperature
Positioning module Powered by bus Communication: GSM
1 prototype station mounted on bus
Vetterli
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LAUSANNE COVERAGE
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DATAFROM LAUSANNE DEPLOYMENT
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ZRICHMOBILE
5 stations mounted on Trams O3, CO, fine particles
Temperature, humidity, acceleration
Communication: GSM, WLAN
Calibration Under lab conditions at EMPA
On-the-fly using 3 reference stations
in the cityPlan: 10 stations
Thiele
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HARDWARE
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INSTALLATION @ TRAM 3005
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ZRICH DATA
CO concentration PM concentration
Pollutant # of Measurements Sampling rate Time Period
Particulate matter 116000 5s 2 months
Ozone 350000 20s 5 months
CO 350000 20s 5 months
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PERSONAL MOBILE SENSOR
ThieleD. Hasenfratz, O. Saukh, S. Sturzenegger, and L. Thiele. Participatory Air Pollution
Monitoring Using Smartphones. In the 1st International Workshop on Mobile
Sensing: From Smartphones and Wearables to Big Data, Beijing, China, 2012.
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GSN @ DATA.OPENSENSE.ETHZ.CH
Thiele, Aberer et al
Thiele Aberer
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2. DATA ANALYSIS
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OPTIMAL SENSINGFOR MOVING SENSORS
Goal: find an optimal sensing strategy, which provides an appropriate balancebetween maximize sensing coverage ofmoving sensors and minimizesensing cost (sampling)?
Question: Can segmentation help?
Aberer Z. Yan, J. Eberle, K. Aberer, OptiMoS: Optimal Sensing for Mobile Sensors, 13thInternational Conference on Mobile Data Management (MDM). Bengaluru, India,July 2012
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RESULTS FOR LAUSANNE DATA Segmentation
Evaluated heuristicsegmentation strategies
Compared to optimal
On existing datasets about 5segments sufficient
Sampling
Evaluated heuristic strategies
Entropy-based strategyperforms best
Current sampling rate about 5times too high
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REGION-BASED MODEL Observation
pollution tends to be relativelyhomogeneous within regions
Approach Tracking trends of pollution level in
regions (streets, residential blocks,
parks) Annotate regions with relevant
emission, land use, meteorology andmeasurement data.
Goals learning structured causal relations
from past data Interpolate real time measurement Understand possible causes of
pollution levels
FaltingsJ. J. Li, B. V. Faltings, Towards a Qualitative, Region-Based Model for Air Pollution
Dispersion, IJCAI Workshop on Space, Time and Ambient Intelligence (STAMI),
2011.Thiele
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NON-STATIONARY GAUSSIAN MODEL Interpolate with a global non-
stationary spatial model based onGaussian Process regression
Learning spatial covariance frompast data
Validate model with Strasbourgsimulations
A fully modular java toolkit formodeling/visualizing pollution.
J. J. Li, B. Faltings, O. Saukh, D. Hasenfratz, and J. Beutel. Sensing the Air We Breathe theOpenSense Dataset. In the Proceedings of the 26th International Conference on ArtificialIntelligence (AAAI), Toronto, Canada, 2012.
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3. USER CONCERNS
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SEMANTIC INDOOR ACTIVITIES:
LEARNINGFROM ACCELEROMETER
Dataset
Nokia N95
6 users
1-2 months daily indoor activities
User activity tag cloud
Aberer
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TWO-TIER SEMANTIC ACTIVITY LEARNING
Robust feature set for Micro Activity inference sit, stand, walk, sitActive, loiter etc.
Set/Sequential features for Semantic Activity inference Home activities: cook, work, relax, break, eat, baby-care, etc.
Office activities: work, lunch, break, meeting, toilet, coffee, etc.
Raw
Accelerometer
Stream
a1
a2
an
an+1
an+2
an+m
GPS Location: office GPS Location: home
Layer I. Micro-Ac vity Inference [MA](Classifica on using Frame-Level Raw Accelerometer Features)
Layer II. High-level (Seman c) Ac vity Inference [HA](Discrimina ve Set & Sequen al Pa ern Features on MA streams)
HA1=break@office HA2=cooking@home
Inferred MA
Streams
Inferred HA
Labels
Frame Frame Frame FrameFrame Frame Frame FrameFrame Frame
Z. Yan, D. Chakraborty, A. Misra, H. Jeung, K. Aberer, SAMMPLE: Detecting Semantic
Indoor Activities in Practical Settings using Locomotive Signatures, 16th International
Symposium on Wearable Computers (ISWC), Newcastle, UK, June 2012
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Participatory sensing
Users reveal location
Semi-honest aggregation serverinfers user activity
Obfuscation affects data quality
Approach
Personalized privacy
Users estimate potential privacy loss
USER PRIVACYVS. DATA RELIABILITY
Possiblemoves
Obfuscatedtrajectory
D(t)= dist(postrue(t),Y(t))P(Y, t)Y posobf(t)
Aberer B.Agir, T.Papaioannou, R.Narendula, K.Aberer, J.P. Hubaux.,An adaptive scheme for personalized privacy in participatorysensing, WiSec 2012.
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EXPERIMENTAL RESULTS
Real data for electrosmog sensing by Nokia campaign
Avg Static : static parameters that meet the threshold on the average
Max Static : static parameters that always meet the threshold
error completeness
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4. END-TO-END SYSTEM ARCHITECTURE
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CONTROL: WHATISTHEPROBLEM?
1. Node decides individually depending on its state, e.g. calibration
2. Nodes communicate with WSN and coordinate
3. Base station schedules nodes using mobility model: a third node arrives,dont measure!
4. Air quality model: dont need measurement!
5. Privacy model: node 1 should measure!6. Application model (e.g. health service):
no measurement needed!
Two mobile nodes:who should measure?
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Approach
Data aggregator produces amodel cover from a set ofmodels on an area
Continuous sensor updates Continuous and ad-hoc queries
Goal Consider privacy concerns Account for trustworthiness Optimize social utility
MULTI-MODEL QUERY PROCESSING
Continuous Moving Queries
Give a (in car) pollution updateevery 30 mins
Aggregate Queries
COX emitted yesterday inLausanne center
Aberer
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Sensors
2D region
Randomly moving sensors
Inherent sensor inaccuracy
Not completely trusted
Users
Ask point queries
Obfuscate their location bymultiple requests
Limited budget
Data aggregator
Collects queries
Selects sensors
Optimizes using a utility function
Strategies
Greedy: iteratively select bestsensor for each query
PerLocation: iteratively selectbest sensor for each location
Randomized: repeat PerLocation
for different location orderings
SIMULATION STUDY
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SENSOR SELECTIONHEURISTICS
0
10
20
30
40
50
60
70
80
90
Trustworthy Trustworthiness_Uniform_[0.5,1] Trustworthiness_Uniform_[0,1]
TotalUtility
Trustworthiness of Sensors
Total utility of different sensor selection algorithms for different
trust distributions of sensors
Optimal
Randomized
PerLocationOptimal
Greedy
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SOCIAL UTILITY
0
5000
10000
15000
20000
25000
30000
TotalUtility
Privacy Sensitivity-Energy Cost Function
Total utility by the greedy algorithm for different sensitivity toprivacy, different trust distributions and energy cost functions
Trustworthy
Trustworthiness_Uniform_[0.5,1]
Trustworthiness_Uniform_[0,1]
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OPENSENSE ARCHITECTURE
cleanedcalibrated
data
Mobile sensors
Data
aggregation server
measurements,location, status
Environment modelsinterpolation/segment
ation
Applications
response
Data Flow
schedule(measurements, priority)
Schedulingcomponent
local coordination
sampling for locationsconsidering error, value
Service market
charges data costsubmits offers
checks data offerssubmits requests
queries
Data market
required samplespriority
Control Flow
landuse data
Mobility model sensor locations
predictions
Calibration modelCleaning model
Sensor model(e.g sensor wear)
Map dataLanduse data
sensor status
predictions
raw data
calibrated data
Context
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CONCLUSIONS
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COLLABORATIONS
e2V,SensorScope
, EMPA,FHNW
Sensing platformVetterli,
Martinoli,Thiele
TL, VBZ,PSA
Mobility platform Vetterli,Martinoli,
Thiele
Nokia,CHUV,
Swiss TPHApplications
Vetterli,Faltings,Aberer,Blom
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CONCLUSION
End-to-end system view crucial Investigate all system layers: sensor user interfaces
Utility-based framework as integrative approach
Availability of real data and user requirements crucial Last year will focus in particular on integration
Having a concrete health related scenario in mind
Results applicable beyond air pollution Complex, distributed, participatory measurement
For more information: opensense.epfl.ch
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TEAM Karl Aberer, EPFL-LSIR, project
leader
Thanasis Papaioannou, postdoc
Rammohan Narendula, PhD
Mehdi Riahi, PhD
Zhixian Yan, PhD Sofiane Sarni, engineer
Saket Sathe, PhD
Boi Faltings, EPFL-LIA, PI
Jason Jingshi Li, postdoc
Martin Vetterli, EPFL-LCAV, PI
Guillermo Barrenetxea, postdoc
Andrea Ridolfi, postdoc
Alcherio Martinoli, EPFL-DISAL, PI Chris Evans, PhD
Emanuel Droz, engineer
Adrian Arfire, PhD
Lothar Thiele, ETH Zrich, PI
Olga Saukh, postdoc Jan Beutel, postdoc
David Hasenfratz (PhD)
Christoph Walser (engineer)