XSense, Nano-Tera annual conference 2013

7/28/2019 XSense, Nano-Tera annual conference 2013

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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).


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.


Thiele: The Problem Bi t, DCOSS 2013

(BPA).


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.


qualitative interpretation.

No precise geometric

information available.


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With X-Sense Infrastructure and Fusion


quantitative interpretation.

Precise geometric information

retrievable.


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?

L L d


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Lessons Learned

Establishing a dependable complete physical pipeline and

virtual data pipeline is a challenge:

organization, people, cultures, engineering, science

L L d


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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

XSense, Nano-Tera annual conference 2013

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