Post on 28-Feb-2021
Signals, Instruments, and Systems – W11Environmental Monitoring –Sensor Nodes and Networks
in Real Deployments
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Outline• Static sensor networks for
microclimate monitoring: the Sensorscope project– Motivation– System design– Practical issues
• Mobile sensor networks for air quality monitoring: the OpenSense project– Goals and research questions– System design– Lausanne and Zürich deployments
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Motivation for Sensor Networks
What if we could monitor events which …
– have a large spatial and temporal distribution– require in-situ measurements– take place in hard to access places– generate data which need to be available in
real-time
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Motivation for Sensor Networks
What would we need for that?A device which …
– is cheap – so we can distribute many of it – is reliable – so we can measure for a long time– uses little power – battery/solar cell powered– has a radio – so it can communicate– can potentially move – so it can potentially
relocate
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Building a SensorNetwork: Key Concepts
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Introduction
Temperature
Humidity
Light
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Topology
?
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Topology
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Topology
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Topology
GPRS
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Topology
Pros• Very simple!• No restrictions in sensor locations
Cons• The server may be quite far from the stations• A long-range link may consume a lot of energy!
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Topology (TinyNode Example)
0
200
400
600
800
Sensor MSP430 XE1205 GPRS
Power
Consum
ption[m
A]
0.5 350
700!
14xtim
estheXE
120 5
!
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Topology (SensorScope Example)
0
200
400
600
800
Sensor MSP430 XE1205 GPRS
Power
Consum
ption[m
A]
0.5 350
700!
14xtim
estheXE
120 5
!
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Topology
Assuming four AA batteries, 1.2 V, 2000 mAh
• Sensor: 167 days• MSP430: 28 days• Short range radio: 1.7 days• Long range radio: 8 hours
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Topology
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But makes sense on projects such OpenSense!
Topology
GPRS
Short range
Sink
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TopologyRecall Friis law (week 10):
Example: To transmit over 5 Km we can using 868 MHz we can:
• One hop of 5 km: L = 106 dB• Two hops of 2.5 km: L = 99 dB• Five hops of 1 km: L = 92 dB
Energy is the main issue !!!
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Multi-hop Sensor Network
GPRS
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Multi-hop Sensor Network
Pros• Only one car battery in the network• The sensor network has extended monitoring coverage• Multiple routes for stations to communicate with the sink• Auto configurable network (robustness)
Cons• Significantly more complicated• Data rate is not increased• Unable to use directional antennas
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Field Deployment of Static Sensor Networks
–The SensorScope Project
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Microcontrollers are inside hermitically sealed boxes, attached on a mounting pole with up to seven external sensors.
Price: 4000-5000 CHF
SensorScope
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SensorScope
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Shockfish TinyNode with TinyOS 2.xMSP430 16-bit microcontroller @ 8MHz48KB ROM, 10KB RAM, and 512KB flash memorySemtech XE1205 radio transceiver @ 868MHz, 76Kbps
SensorScope
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162x140mm solar panel12Ah NiMH rechargeable battery
SensorScope
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SensorScopeMany previous successful deployments
97 stations deployed at EPFL (one year)
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SensorScopeMany previous successful deployments
16 stations deployed at Le Génépi to monitor conditions leading to dangerous mudslides (two months)
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Field Deployment of Mobile Sensor Networks:
–The OpenSense Project
Mobile sensors (parasitic, uncontrolled mobility) on public transportation vehicles
Static wireless sensing and communication infrastructure
OpenSenseCommunity-driven, large-scale air pollution
measurement in urban environments
Sensorscope
Permasense
• 2% of all deaths (1.2 million people)• 0.6% of burden of disease (DALY)
Urban air pollution
Global Health Risks, WHO 2009World Urbanization Prospects, U. N. 2008
Urban population will double in next decades
Motivation
• > 50% of world population already lives in cities• rural population expected to stagnate or drop
Fine resolution air quality data is needed!
Enabling research in:• Human exposure• Air Pollution Engineering• Urban Planning• Environmental Justice• Public Policy
Public service & education• enable private users to make
informed decisions• raising popular awareness
MotivationAir pollution is highly location-dependent• traffic chokepoints• urban canyons• industrial installations
Sparse networks of ground stationsExample: Switzerland’s NABEL (www.empa.ch/nabel)
• 16 stations
• specially selected sites
urban with traffic urban residential suburban rural, etc.
• resolution: high temporal low spatial
Mission: monitor air pollution on national level & gauge impact of environmental policies
Public data access: http://www.bafu.admin.ch/luft/luftbelastung/blick_zurueck/datenabfrage
Traditional Air Monitoring Systems
Ozone concentration
Station locations
Satellite-based remote sensingExamples:• Measurements of Pollution in the Troposphere (MOPITT on Terra satellite)• Ozone Measurement Instrument (OMI on Aura satellite)
Features:• daily scans• large coverage• homogeneous quality• sensitive to cloud coverage• low resolution
Traditional Air Monitoring Systems
• mobile sensor network• parasitic mobility: anchored to existing mobility sources
• low-cost, light-weight chemical (CO, CO2, NO2, O3) & ultrafine particle (UFP) sensors• intelligent integration & control to mitigate demanding constraints
- vehicle energy supply - predictable mobility- single point maintenance
public transport
OpenSense System
SENSING SYSTEMFrom many wireless, mobile,heterogeneous, unreliable rawmeasurements …
INFORMATION SYSTEM… to reliable, understandable and
Web-accessible real-time information
NA
NO
TE
RA
Nabel station Zürichstatic nodesmobile nodes
GPRSGPS
sensor network controloptimization of data acquisition
information dissemination
Proposed System
• Traditional approach Few stations Low resolution interpolated
estimates of pollutant concentrations across massive regions
• Recent results Massive deployment of stations (150)
at street-level (2008/2009 New York City Community Air Quality Survey)
Pollutants of interest heavily concentrated along roads with high traffic densities
Value of Dense Measurements
Global questions:• More data, more noise, but also more redundancy
Can we produce better quality data?• Case study for other environmental phenomena:
Radiation, noise, energy
Research directions:• Wireless Sensor Network control
When/Where to sample? What/To whom to transmit?
• Sensor Node design Sampling System Localization Software & hardware architecture Mechanical integration
Challenges
• Community sensing privacy protection trustworthiness of data, relevance of data gathered and
information produced• Modeling
sensor, device and mobility models air quality models privacy, trust & activity models
Gas Sampling System
Open sampling• sensors directly exposed to environmental
measurandBenefits:• simple & “slim” solution• continuous samplingDrawbacks:• no absolute concentration values• noisy signal (sensitive to environment
variations: pressure, humidity)Typical response:
Closed sampling• sensors exposed to measurand inside controlled
chamber• 3-phase strategyBenefits:• absolute measurements• noise due to environment filteredDrawbacks:• complex & bulky• non-continuous samplingTypical response:
[Lochmatter 2010] [Trincavelli 2010]
Idea: Combine these two approaches to get the benefits of both systems.
Problem:Chemical sensors have very slow dynamics (example: Telaire 6613 CO2 sensor step response <2min)
• Smart sampling module possibly hybrid single/multi-chamber wind sensing
controlled flow
uncleanair
cleanair
open
closed
passive active
[Lochmatter et al. 2010]
[Gonzalez-Jimenez et al. 2011]
Current deployment
Gas Sampling System
[Lochmatter et al. 2010]
Anemometer
Logger• GPRS link to back-end server• local storage on SD card
Robust localization – prerequisite for adaptive control• exploits commercial state of the art u-blox LEA-6R
GPS + dead reckoning (DR) module• augmentation with additional sensor modalities
GPS only
GPS + DR
Logging & Localization
logger
localization
Localizationdoors open Current stop: Sallaz
Next stop: Valmont
Next stop: Sallaz• large set of rich data:
location parameters (geographical coordinates, heading, odometer, speed, acceleration etc.)
vehicle context data
OpenSense Lausanne Node
Particle sampling module• Ultrafine particle
measurements using NaneosPartector
• Measures directly lung-deposited surface area
Gas sampling module• CO, NO2, O3, CO2,
temperature & relative humidity
• Hybrid active sniffer/closed chamber sampling operation
• Enables absolute concentration mobile measurements
Enhanced localization & logger• mounted inside bus• Fused GPS, gyro and vehicle
speedpulses• Accurate sample geolocation even in
difficult urban landscapes• GPRS communication
On top of 10 buses in Lausanne• CO, NO2, O3, CO2, UFP, temperature, humidity• Active sniffing & closed sampling system• Localization: Augmented GPS; communication: GPRS• Prototypes deployed in multiple stages since June 2011• Full deployment: Since November 2013
At NABEL roadside station in Lausanne• Calibration and sensor drift evaluation• Testing new sensors• Since June 2010
On top of C-Zero electric vehicle• 100% electric, clean platform• flexible mobility• system test bed• targeted investigation tool• intelligent network servicing
Lausanne Deployment
OpenSense Zurich Node
Inside the OpenSense Zurich node
OpenSense Zurich node
Installation on top of VBZ Cobra tram
Static OpenSense stationat NABEL station for sensor tests
Luftibus with OpenSense stationcovers whole Switzerland
10 streetcars in Zurich equippedwith OpenSense stations
At NABEL station in Dübendorf• Long-term sensor testing (e.g., O3)• Testing new sensors (combined CO/NO2)• Since April 2011
On top of 10 streetcars in Zurich• O3, CO, ultrafine particles, temperature, humidity• Localization: GPS; Communication: WLAN and GSM• Since September 2011
On top of “LuftiBus”• O3, ultrafine particles, temperature, humidity• Localization: GPS; Communication: GSM• Since March 2013, covers whole Switzerland
Zürich Deployment
CO concentration UFP concentration
Pollutant # of Measurements Sampling rate Time Period
UFP 56.000.000 5s 22 months
Ozone 8.900.000 20s 22 months
CO 5.300.000 20s 22 months
[Keller et al., SenseApp 2012]
Pollution Data – Zurich Deployment
Pollution Data – Lausanne Deployment
CO concentration UFP concentration
Pollutant # of Measurements Sampling rate Time Period
UFP 9.151.000 1s 3 months
Ozone 2.488.000 15s 5 months
[CO, NO2, CO2] 10.930.000 5s 5 months
[Arfire, unpublished, 2014]
Mobility Data – Lausanne Deployment
Coverage of Lausanne regionX-axis acceleration& vehicle context
Measurement # of Measurements Sampling rate Time Period
[GPS, gyroscope] 52.207.000 1s 5 months
[odometer, accelerometer] 211.767.000 0.25s 5 months
vehicle context info 751.000 event-driven 5 months
Gyro yaw rate
Next StopCurrent Stop
[Arfire, unpublished, 2014]
Martinoli, Thiele –Stations and mobility
Aberer, Faltings –Data, Models, Trust, Privacy
OpenSense
Krause – CrowdSensing for quakes
Emmenegger – Air quality measurement and modeling
1E5
2E5
3E5
4E5
particle
s/cm3
08:0009:00
10:0011:00
12:0013:00
14:0015:00
16:0017:00
Bochud, Riediker – Studies on health impact of air quality
OpenSense2
Phase 2: Crowdsourcing,dispersion modeling & “closing the loop”
Conclusion
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Take Home Messages• Sensor networks enable environmental monitoring in remote locations
and of difficult-to-measure processes• Real-world deployments may be highly unpredictable!• Mobile sensor networks can increase coverage and spatial resolution of
measured data• Increasing the resolution of air pollution data is necessary for
understanding health impact.• Whether data extracted from poor quality measurements can be
processed to obtain useful data on air pollution is an important research question.
• Other questions: How to design the node? How to control the network?• Using existing mobility sources holds important benefits, but achieving
a reliable system integration is non-trivial.
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Additional Literature – Week 11Environmental engineering applications• Static sensor networks
- Sensorscope: http://sensorscope.epfl.ch/- Swiss Experiment
http://www.swiss-experiment.ch/- Permasense: http://www.permasense.ch/- GITEWS: http://www.gitews.de- WiSARD network: http://wisardnet.nau.edu/- CENS: http://research.cens.ucla.edu/
• Mobile sensor networks- OpenSense: http://opensense.epfl.ch- CENS: http://research.cens.ucla.edu/urbansensing/ 52
Environmental engineering applications• Robotic sensor nodes and networks
– Aquatic microbic observing systems http://robotics.usc.edu/~namos/index.html
– Adapting sampling of oceans http://www.princeton.edu/~dcsl/asap/
– Robots and sensor networks systems for underwater monitoring http://groups.csail.mit.edu/drl/wiki/index.php/AMOUR
– http://research.cens.ucla.edu/mas/– http://research.cens.ucla.edu/aquatic/– IEEE Robotics and Automation Magazine, special issue on
Robotics for Environmental Monitoring, M. Dunbabin and L. Marques, editors, March 2012
Additional Literature – Week 11
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Civil engineering applications• Static sensor networks
– Structural Health Monitoring http://enl.usc.edu/projects/shm/index.html
– Structural Health Monitoring http://www.empa.ch/plugin/template/empa/93/*/---/l=2
- Structural Health Monitoring
http://www.eecs.berkeley.edu/~binetude/ggb/- Structural Health Monitoring
http://research.cens.ucla.edu/projects/2007/Seismic/Tall_Special/
Additional Literature – Week 11
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Additional Literature – Week 11• Static sensor & actuator networks
– Structural Controlhttp://people.ce.gatech.edu/~ywang/research.htm#_WirelessControl
– Structural Controlhttp://imacwww.epfl.ch/Common/research-en.jsp
– Structural Control for wind effect mitigationhttp://jahia-prod.epfl.ch/cms/site/disal2/op/edit/page-32507.html
– Structural Controlhttp://www-personal.umich.edu/~jerlynch/index.html
• Mobile sensor networks– Monitoring of water pipe networks
PipeProbe: http://mll.csie.ntu.edu.tw
Civil engineering applications
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