xsense, nano-tera annual conference 2013
TRANSCRIPT
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X-Sense
Lothar Thiele, Jan Beutel ETH Zurich, Embedded/Wireless
Stephan Gruber University Zurich, Physical Geography
Alain Geiger ETH Zurich, Geodesy and Photogrammetry
Tazio Strozzi GAMMA SA, SAR Remote Sensing
Hugo Raetzo BAFU/FOEN
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What drives us?
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Gurtnellen, Uri,
5.6.2012
1 fatality, rail track closed
for over 1 month
Societal Applications
Eiger
Unterer Grindelwaldgletscher
We do not understand the
underlying geophysical processes.
We can not provide reliable earlywarning systems.
Felbertauern
14.5. 2013
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The X-Sense System
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Before X-Sense
Traditionally geo-scientists operate with
Manual field-campaigns: Few measurement points in time
[Lambiel] or space [van de Wal], often short duration
Expensive, heavy weight infrastructure: Inclinometer
measurements in boreholes [Arenson], [Haeberli]
Applied industrial wireless networking: alpEWAS [Singer, TUM]
Wireless Sensor Networks
Short lived: SensorScope [Vetterli], Volcanoes [Welsh]
Unreliable: Redwoods [Culler], Potatoes [Langendoen]
Low data quality: Great Duck Island [Szewczyk]
Theory: Driven by Smart Dust
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X-Sense Hypothesis
Anticipation of future environmental
states and risk benefits from environmental sensing at
diverse modalities and scales,
process modeling
Wireless Sensor Network Technology allows to quantify mountain phenomena,
can be used forsafety critical applications in an hostile
environment
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The Spatial Pipeline
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Our Sensor Developments
GPS Logger Large-scale, early
access data
GPS CoreStation Experimentation, variable use
Wireless GPS Sensor Fully integrated, low-power
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Deployment Matter Valley
Field Site Inventory
10 GPS on composite landslides
10 GPS on rock glaciers5 GPS as position reference stations
5 simple temperature loggers
per GPS station
2 Meteo stations
3 Cameras
2 High-resolution cameras
1 High-resolution camera robot
Installation started August 2010,
full operability from August 2011
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The Functional Pipeline
2010 2010-2011 2011-2012
individual publications 1 38 32
joint publications 0 6 7
presentations (talks/posters) 2 43 53
raw
sensor
data
pre-processing
data cleaning;system health
formal
system models
locationextraction
weather &
atmosphere
models
geophysicalprocesses
models &
simulation
understanding
processes
predictions
communication
530.000.000 raw data points 117.4 GB
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Research HighlightsComputer Engineering and Networks
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WSN Design and Development Methods
Threats to predictability
non-deterministic environment (energy harvesting, availability
of communication)
working close to resource limits (energy, memory, bandwidth)
makes systems extremely fragile.
formal methods verification
correct by construction
testing
increase observability
distributed and scalable
different modalities
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FlockLab Testbed
Observer
Wired and wireless observation layer
Fast, distributed tracing and actuation oflogic
Synchronized powertracing
Sensorstimuli and references
Time synchronization to ~ s
Wired and wireless observation layer
Fast, distributed tracing and actuation oflogic
Synchronized powertracing
Sensorstimuli and references
Time synchronization to ~ s
Target
Roman Lim, Federico Ferrari, Marco Zimmerling,
Christoph Walser, Philipp Sommer and Jan
Beutel: FlockLab: A Testbed for Distributed,
Synchronized Tracing and Profiling ofWireless Embedded Systems, IPSN 2013.
Roman Lim, Federico Ferrari, Marco Zimmerling,
Christoph Walser, Philipp Sommer and Jan
Beutel: FlockLab: A Testbed for Distributed,
Synchronized Tracing and Profiling ofWireless Embedded Systems, IPSN 2013.
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WSN Communication (before X-Sense)
Unreliable wireless channel leads to
frequent network updates
high power consumption low end-to-end success rate
More nodes and mobility
make the system
more fragile
This holds for all known
protocols, e.g. CTP+{CSMA, LPL, A-MAC}, Dozer, BCP,
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The Crazy Idea: Wireless Bus
Federico Ferrari et al: Low-Power
Wireless Bus. In SenSys 2012.
Federico Ferrari et al.: Efficient
Network Flooding and TimeSynchronization with Glossy.
IPSN 2011 (BPA).
Federico Ferrari et al: Low-Power
Wireless Bus. In SenSys 2012.
Federico Ferrari et al.: Efficient
Network Flooding and TimeSynchronization with Glossy.
IPSN 2011 (BPA).
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Before X-Sense
Example:
Matterhorn Deployment
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Approach: Model-based Validation
Artifacts observed
Packet duplicates, packet loss, wrong ordering
Variations in received vs. expected packet rates
Necessitates further data analysis/cleaning/validation
health status
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Missing Global Network State
Along which path did the yellow packet travel?
Where did the red packet stay for the longest time?
Why was the purple packet traveling slower than the red packet?
Transmitting required information in-band would be too expensive
?
?
?
How can we efficiently retrieve missing network state ?
Packet stream
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Basic Approach
Per-packet information
Source node address
First-hop receiver address
Packet generation time (imprecise)
Arrival time at the sink
Generation sequence at source
Per-packet information
Source node address
First-hop receiver address
Packet generation time (imprecise)
Arrival time at the sink
Generation sequence at source
Source
First-hop
receiver
Sink
Offline Analysis: While traversing the network,
topology, timing and ordering information of
forwarded packets is inferred from locally
generated packets.
Offline Analysis: While traversing the network,
topology, timing and ordering information of
forwarded packets is inferred from locally
generated packets.
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Tested on X-Sense Deployments
10-40 TinyNode nodes running Dozer
Tested on several installations:
Matterhorn, 2008, >78 million received packets
Jungfraujoch, 2009, >48 million received packets
Dirruhorn, 2010, >20 million received packets
Aiguille du Midi, 2012
Matthias Keller, Jan Beutel, Lothar
Thiele: The Problem Bi t, DCOSS 2013
(BPA).
Matthias Keller, Jan Beutel, Lothar
Thiele: Uncovering Routing Dynamics
in Deployed Sensor Networks withMulti-hop Network Tomography,
SenSys 2012.
Matthias Keller, Lothar Thiele, Jan
Beutel: Reconstruction of the Correct
Temporal Order of Sensor Network
Data, IPSN 2011.
Matthias Keller, Jan Beutel, Lothar
Thiele: The Problem Bi t, DCOSS 2013
(BPA).
Matthias Keller, Jan Beutel, Lothar
Thiele: Uncovering Routing Dynamics
in Deployed Sensor Networks withMulti-hop Network Tomography,
SenSys 2012.
Matthias Keller, Lothar Thiele, Jan
Beutel: Reconstruction of the Correct
Temporal Order of Sensor Network
Data, IPSN 2011.
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Research HighlightsGeodesy and Photogrammetry
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Before X-Sense
Pictures are used for
qualitative interpretation.
No precise geometric
information available.
Pictures are used for
qualitative interpretation.
No precise geometric
information available.
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With X-Sense Infrastructure and Fusion
Pictures are used for
quantitative interpretation.
Precise geometric information
retrievable.
Pictures are used for
quantitative interpretation.
Precise geometric information
retrievable.
Camera perspective, using extrinsic
camera calibration on 9 GPS targets
Neyer, F., A. Geiger: Visualizing
vector data: Clustering noisy
displacement fields. Swiss
Geoscience Meeting 2012.
Neyer, F., A. Geiger: Visualizing
vector data: Clustering noisy
displacement fields. Swiss
Geoscience Meeting 2012.
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Fusion: From Image to Displacements
4.58 pixel
18 days,
Oct 2012
Before
X-sense
GPS stations
Fabian Neyer, GGL, 2013
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Fusion: From Image to Displacements
14 cm
18 days,
Oct 2012
After
Fabian Neyer, GGL, 2013
X-sense
GPS stations
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Before X-Sense
Episodic solutions
Episodic obspost
processing
Episodic coordinatesLinear deformation model
Very low temporal
resolution:
in this example10 month
High spatial
accuracy:
2~3 mm
GPS data
Displacement East
Displacement North
Displacement Height
Velocity East
Velocity North
Velocity Height
1.Obs 2.Obs
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X-Sense High Spatial Resolution
Daily solutions
Databasepost
processing
daily coordinates
Low temporalresolution: one
position per day
High spatialaccuracy:
2~3 mm
GPS data
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X-Sense High Temporal Resolution
Real-time epoch-wise solutions
GPS receiversepoch-wise
solutions
real-time
processing
Positions in real
time: 1 per 30s
Decreased
spatial accuracy:
< 1cm
on-line
data stream
Su Z. Geiger A.; Limpach
P.. Investigation on the
performance of low-
cost s ingle-frequency
GPS, International Conf.
on Machine Control andGuidance, 2012.
Su Z. Geiger A.; Limpach
P.. Investigation on the
performance of low-
cost s ingle-frequency
GPS, International Conf.
on Machine Control andGuidance, 2012.
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X-Sense Error Mitigation
Identify errorsmultipath effects
Model errorscreate multipath
template
Correction appliedremove multipath
errors
East
North
Up
Antenna Pattern: Estimation of Antenna Phase Center Variation
Multipath Identification
Antenna Pattern: Estimation of Antenna Phase Center Variation
Multipath Identification
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Research HighlightsGeoscience
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Before X-Sense
Limited availability of evidence
Vast, heterogeneous terrain; diversity
Dominantly manual data collection
Coarse temporal/spatial granularity
Models for interpretation
are rudimentary, e.g. sinusoidal
Models cannot be applied to other scenarios,
e.g. at large scale
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X-Sense delivers data at
unprecedented levels of detail
Spatial scale 25 GPS stations
Temporal scale 30 sec intervals
High accuracy cm to mm scale
Changing Opportunities with X-Sense
Mean annual velocity
0.1 m/year
2 m/year
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X-Sense delivers data at
unprecedented levels of detail
Spatial scale 25 GPS stations
Temporal scale 5 sec intervals
High accuracy cm to mm scale
Changing Opportunities with X-Sense
Mean annual velocity
0.1 m/year
2 m/year
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Large Variability Requires Filtering
Large scale of variability &
noise found in velocity signals
computed from GPS positions
To distinguish signal fromnoise, simple methods
(splines) do not work
Parameterization is
problematic where strongchanges in behavior occur
Monte-Carlo simulation allows
estimating noise as basis forvariable-support smoothing
Method performs well in noisy
data with variable velocity
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Detailed Interpretation of Terrain Motion
10
5
0
5
10
15
20
GST[degC]
11.09.01 11.11.01 12.01.01 12.03.01 12.05.01 12.07.01
0.005
0.010
0.015
0.020
0.025
0.030
0.035
velocity[m
/d](SNR:30)
GST hor vel
warming without
zero-curtain
warming without
zero-curtain
warming and
zero-curtain
warming and
zero-curtain
Smooth change of velocity
phase-lagged to temperature
Smooth change of velocity
phase-lagged to temperature
fast acceleration,
before complete
snow melt
fast acceleration,
before complete
snow melt
fast but weak
acceleration
fast but weak
accelerationMarc-Olivier Schmid, Stefanie Gubler, Joel Fiddes and Stephan Gruber:Inferring snow pack ripening and melt out f rom distributed ground surface
temperature measurements, The Cryosphere, 6, 11271139, 2012.
Marc-Olivier Schmid, Stefanie Gubler, Joel Fiddes and Stephan Gruber:Inferring snow pack ripening and melt out f rom distributed ground surface
temperature measurements, The Cryosphere, 6, 11271139, 2012.
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Temperature of soil and snow cover
Liquid water content of soil and snow cover
Observations Lead to Simulations
GEOtop simulator
Atmosphere interaction
Multi-layer snow pack Lateral drainage
Frozen soil
Water budget: saturatedand unsaturated
Topography
Can be driven by globalclimate data
Simulation experiments point
to need for dual porosity model
deep percolation only
several days after melt
acceleration detected before
snow pack has melted
Stefanie Gubler, Stefano Endrizzi, Stephan Gruber and
Ross Purves: Sensit ivity and uncertainty of modeled
ground temperatures and related variables in
mountain environments, Geosci. Model Dev. Discuss.,
6, 791-840, 2013
Stefanie Gubler, Stefano Endrizzi, Stephan Gruber and
Ross Purves: Sensit ivity and uncertainty of modeled
ground temperatures and related variables in
mountain environments, Geosci. Model Dev. Discuss.,
6, 791-840, 2013
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Outreach
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Ecosystem of X-Sense
Collaborations and Partner Activi ties
Snow and Permafrost, Marcia Philips (SLF
Davos)
EDYTEM, Philip Deline, Ludovic Ravanel
(Universite de Savoie, Chambery, France)
LGIT, David Amitrano (Universite Joseph
Fourier, Grenoble, France)
Physical Geography H2K, Philipp Schneider,
Jan Seibert (University of Zurich)
CCES COGEAR project, Jeff Moore, Simon
Loew (ETH Zurich)
CCES APUNCH project, Maurizio Savina,
Paolo Burlando (ETH Zurich)
CCES RECORD project, Philipp Schneider,
Mario Schirmer (EAWAG)
VAW, Andreas Bauder, Martin Funk (ETHZurich)
OpenSense project, Olga Saukh (ETH Zurich)
Volcanology, Thomas Walter (GFZ Potsdam,
Germany)
Alpine Cryosphere and Geomorphology,Reynald Delaloye (University of Fribourg)
Collaborations and Partner Activi ties
Snow and Permafrost, Marcia Philips (SLF
Davos)
EDYTEM, Philip Deline, Ludovic Ravanel
(Universite de Savoie, Chambery, France)
LGIT, David Amitrano (Universite Joseph
Fourier, Grenoble, France)
Physical Geography H2K, Philipp Schneider,
Jan Seibert (University of Zurich)
CCES COGEAR project, Jeff Moore, Simon
Loew (ETH Zurich)
CCES APUNCH project, Maurizio Savina,
Paolo Burlando (ETH Zurich)
CCES RECORD project, Philipp Schneider,
Mario Schirmer (EAWAG)
VAW, Andreas Bauder, Martin Funk (ETHZurich)
OpenSense project, Olga Saukh (ETH Zurich)
Volcanology, Thomas Walter (GFZ Potsdam,
Germany)
Alpine Cryosphere and Geomorphology,Reynald Delaloye (University of Fribourg)
O S
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Dissemination to OpenSense
Mobile deployment in the context of OpenSense
10 stations (O3, CO, and PM & UFP sensors) on public
transportation
> 1 year of measurements and 30 Mio data points
Use of X-Sense CoreStation
and X-Sense Data Pipeline
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What did we learn?
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Lessons Learned
Establishing a dependable complete physical pipeline and
virtual data pipeline is a challenge:
organization, people, cultures, engineering, science
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Lessons Learned
Establishing a dependable complete physical pipeline and
virtual data pipeline is a challenge:
organization, people, cultures, engineering, science
Interesting scientific questions arise from serious
applications. But serious applications involve tremendous
effort in understanding environment and constraints
related science
but it is fun (most of the times)
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Foreigners trash our Matterhorn
Acknowledgement Vid P
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Acknowledgement
People:
Lothar Thiele, Jan Beutel, Stephan Gruber, Alain Geiger, Tazio
Strozzi, Hugo Raetzo, Philippe Limpach
Bernhard Buchli, Stefano Endrizzi, Federico Ferrari, Tonio Gsell,Matthias Keller, Roman Lim, Fabian Neyer, Zhengzhong Su, Felix
Sutton, Samuel Weber, Christoph Walser, Vanessa Wirz, Mustafa
Yuecel, Marco Zimmerling, .
Funding and Support:
VideoPermasense.mov