Audiovisual Communications Laboratory

Signal Processing and Signal Processing and Communications for Sensor NetworksCommunications for Sensor Networks

Martin Vetterli, EPFL and UC Berkeleyjoint work with T. Ajdler, G. Barrenetxea, H. Dubois-Ferriere, I. Jovanovic, R. Konsbruck, O. Roy, T. Schmid,

L. Sbaiz, E.Telatar, M.Parlange (EPFL), P.L.Dragotti (Imperial), M.Gastpar (UCBerkeley)Spring 2007

Work done within the Swiss NSF National Center on Mobile Information and Communication Systemshttp://www.mics.org

Indian Institute of Science Centennial, 29.5.08

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OutlineOutline

1. IntroductionThe Center on “Mobile Information and Communication Systems”Wireless sensor networks: from “one to one” to “many to many”

2. The structure of distributed signals and samplingSensor networks as sampling devicesDistributed image processing: The plenoptic functionSpatial sound processing: The plenacoustic function

3. Distributed source codingSource coding, Slepian-Wolf and Wyner-ZivDistributed R(D) for sounds fields

4. On the interaction of source and channel codingTo separate or not to separate... That is the question!The world is analog, why go digital? Gaussian sensor networks

5. Environmental monitoringEnvironmental monitoring for scientific purposes and sensor tomographySensorScope: Intelligent building and environmental monitoringReal experiences with sensor networks

6. Conclusions

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AcknowledgementsAcknowledgements

• To the organizers!• Swiss and US NSF, our good friends and sponsors

• The National Competence Center on Research‘’Mobile Information and Communication Systems’’ (MICS)

• K.Ramchandran and his group at UC Berkeley,for sharing pioneering work on distributed source coding

• Colleagues at EPFL and ETHZ involved in MICS- J.P.Hubaux, for pushing ad hoc ntws- M.Grossglauser, for making things move- E.Telatar, for wisdom and figures!- J.Bovay, for NCCR matters

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1.1 Introduction1.1 Introduction

The Swiss National Competence Center on Research (NCCR)“Mobile Information and Communication Systems’’ (MICS)

http://www.mics.org

Goal: study fundamental and applied questions raised by new generationmobile communication/information services, based on self-organisation.

Cross-layer investigation: mathematical issues (statistical physics based analysis, information and communication theory) to networking, signal processing, security, distributed systems, software architecture, DB etc

Examples: ad-hoc networks, sensor networks, peer-to-peer systemsNetwork of researchers:

- EPFL, ETHZ, CSEM, UNI-BE,L,SG,ZH, 30 profs, 70 PhD students- 5 clusters, ranging from circuits to applications

Budget:- 8 MSfr/Year (6 M$/Y-> 7 M$/Y)- 12 years horizon (2001-2013)

Note: similar to a US-NSF Engineering Research Center

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The NCCR MICS NetworkThe NCCR MICS Network

University of AppliedSciences Western SwitzerlandUniversity of AppliedSciences Western Switzerland

EPFL: Schools of Computer and Communication Sciences (Leading House), Engineering and Architecture & EnvironmentEPFL: Schools of Computer and Communication Sciences (Leading House), Engineering and Architecture & Environment

University Lausanne:Ecole des Hautes Etudes CommercialesUniversity Lausanne:Ecole des Hautes Etudes Commerciales

University Bern: Institute of Informatics and Applied MathematicsUniversity Bern: Institute of Informatics and Applied Mathematics

University Lugano:Computer Science DepartmentUniversity Lugano:Computer Science Department

University Basel:Computer Science DepartmentUniversity Basel:Computer Science Department

CSEM, Swiss Center for Electronics and MicrotechnologyCSEM, Swiss Center for Electronics and Microtechnology

ETHZ: Electrical Engineering and Computer Science DepartmentsETHZ: Electrical Engineering and Computer Science Departments

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From centralized to From centralized to ““selfself--organizedorganized”” (1/2)(1/2)

• Classic solutions (e.g. GSM, UMTS):characterized by heavy fixed infrastructures

• Evolution of wireless communication equipment: computational power , size , price , ~ transmit power

• 110 Billion US$ for UMTS licenses: is thereanother way?

Ad-hoc networking solution:- multihop, collaborative- reinvented many times- self-organization cute but tricky ; )

Spring 2008 - 7

From centralized to From centralized to ““selfself--organizedorganized”” (2/2)(2/2)

Why not ad-hoc everywhere?- fully multihop solution- sensor networks- Mesh networks- peer-to-peer communications

Current practice-> hybrid solution:

multihop access to backbone

Spring 2008 - 8

Some capacity questionSome capacity question……....

N points (users)

O(N) users O(N) users

Cut set ~ N

O(N) transmissions from left to right

over

O( ) transmission links

mean

O( ) capacity per attempted transmission

N

1N

Gupta/Kumar showed that there might be a capacity problem…!– the total capacity does not scale well with the number of users– it depends on the traffic matrix– the question is hard!

Very active research area- random matrix theory- sophisticated bounding methods

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Some fundamental principleSome fundamental principle……

It “percolates” through connectivity, capacity, P2P, gossip, etc

Percolation Theory as a Fundamental Concept

sub-critical (r slightly < rc) super-critical (r slightly > rc)

rrc

p

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1.2 The view of the world: Wireless sensor networks!1.2 The view of the world: Wireless sensor networks!

Signals exist everywhere...they just need to be sensed!– distributed signal acquisition: many cameras, microphones etc– these signals are not independent: more sensors, more correlation– there can be some substantial structure in the data,

due to the physics of the processes involvedComputation is cheap

– local computation– complex algorithms to retrieve data are possible

Communication is everywhere– this is the archetypical multiterminal challenge– mobile ad hoc networks, dense, self-organized sensor networks are built– the cost of mobile communications is still the main constraint

Cross-disciplinarity– fundamental bounds (what can be sensed?)– algorithms (what is feasible?)– systems (what and how to build?)

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The Change of ParadigmThe Change of Paradigm

Old view: one source, one channel, one receiver (Shannon 1948)

Next view: distributed sources, many sensors/sources, distributed communication medium, many receivers!

Note: many questions are open!

ChannelSource Receiver

sourceschannels

receivers

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The The swissswiss version of homeland security :)version of homeland security :)

Distributed sensor network for avalanche monitoring:

Method: drop sensors, self-organized triangulation, monitoringof location/distance changes, download when critical situation

Challenges: extreme low power, high precision, asleep most of the time, when waking up, quick download... and all self-organized!

Legacy technology: build a chalet, see if it stands after 50 years!

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The The swissswiss version of homeland security (cont.):version of homeland security (cont.):

Avalanche and Landslide Analysis through Sensor Networks(E.Charbon and C.Ancey, EPFL)

Approach– Sensor network moving within natural event

Goals– Gain insight into currently unknown phenomena– Model and validate novel sensor network paradigms– Miniaturize 10GHz UWB local positioning system– Gain experience in distributed warning and monitoring systems

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Environmental Monitoring: Technological Paradigm Change Environmental Monitoring: Technological Paradigm Change Orders of magnitude less cost for sensing:

100K$ 10-100$

Orders of magnitude of difference in price, size and power!We expect this will have a tidal effect on

– what is monitored– how it is monitored– what is understood

and there are Berkeley motes to save the world!(and many other platforms of the sort)

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OutlineOutline

1. Introduction2. The structure of distributed signals and sampling

Sensor networks as sampling devicesDistributed image processing: The plenoptic functionSpatial sound processing: The plenacoustic function

3. Distributed source coding4. On the interaction of source and channel coding5. Environmental monitoring6. Conclusions

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2. The Structure of Distributed Signals and Sampling2. The Structure of Distributed Signals and SamplingA sensor network is a distributed sampling device

Physical phenomena– distributed signals are governed by laws of physics– partial differential equation at work: heat and wave equation…– spatio-temporal distribution

Sampling– regular/irregular, density– in time: easy– in space: no filtering before sampling– spatial aliasing is key phenomena

Note: here we assume that we are interested by the ‘’true’’ phenomena,decision/control: can be different!

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2.1 Sampling the real world2.1 Sampling the real world

We consider 2 ‘’real’’ cases, and follow:– what is the physical phenomena– what can be said on the ‘’discretization” in time and space– is there a sampling theorem– what is the structure of the sampled signal

Light fields– wave equation for light or ray tracing– plenoptic function and its sampling

Sound fields– wave equation for sounds– plenacoustic function and its sampling

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2.2 The 2.2 The PlenopticPlenoptic Function [Function [AdelsonAdelson]]

Multiple camera systems– physical world (e.g. landscape, room)– distributed signal acquisition– possible images: plenoptic function, 7-dim!

Background: – pinhole camera & epipolar geometry– multidimensional sampling

Implications on communications– camera sources are correlated in a particular way– limits on number on ‘’independent’’ cameras– different BW requirements at different locations

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ExamplesExamples

[Stanford multi-camera array]

3D 3D

2D

4D 5D

[Imperial College multi-camera array]

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2.3 The 2.3 The PlenacousticPlenacoustic Function [Ajdler]Function [Ajdler]

Multiple microphones/loudspeakers– physical world (e.g. free field, room)– distributed signal acquisition of sound with “many” microphones– sound rendering with many loudspeakers (wavefield synthesis)

This is for real!– sound recording– special effects– movie theaters (wavefield synthesis)– MP3 surround etc

MIT1020 mics

LCAV 8 LS, moving mics

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PlenacousticPlenacoustic function and its samplingfunction and its sampling

Setup

Questions:– Sample with “few” microphones and hear any location?– Solve the wave equation? In general, it is much simpler to sample the

plenacoustic function– Dual question also of interest for synthesis (moving sources)– Implication on acoustic localization problems– Application for acoustic cancellation

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Examples:Examples:

PAF in free fields … in a room for a certain point source

• We plot: p(x,t), that is, the spatio-temporal impulse response• The key question for sampling is: , that is, the Fourier transform• A precise characterization of for large and will allow sampling

and reconstruction error analysis

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PlenacousticPlenacoustic function in Fourier domain (approx.):function in Fourier domain (approx.):

Sampled Version:

Thus: Spatio-temporal soundfieldcan be reconstructed up to ω0

ω:: temporal frequency

Φ: spatial frequency

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Computed and Measured Computed and Measured PlenacousticPlenacoustic FunctionsFunctions

• Almost bandlimited!• Measurement includes noise and temperature fluctuations

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A sampling theorem for the A sampling theorem for the plenacousticplenacoustic functionfunction

Theorem [ASV:06]:• Assume a max temporal frequency• Pick a spatial sampling frequency• Spatio-temporal signal interpolated from samples taken atArgument:

• Take a cut through PAF• Use exp. decay away from central triangle to bound aliasing• Improvement using quincunx lattice

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Some generalizations: The EM caseSome generalizations: The EM case

Electromagnetic waves and UWB• Wave equation• 3 to 6 GHz temp. frequency• And a triangle!

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The EM case and TV channelsThe EM case and TV channels

Assume a movement model

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On sampling and representationOn sampling and representation

We saw a few examples:– Plenoptic function and light fields– Plenacoustic function and sound fields

It is a general phenomena– Heat equation– Electromagnetic fields– Diffusion processes

This has implications on:– Sampling: where, how many sensors, how much information is to be sensed– Gap between simple (separate) and joint coding– Spatio-temporal waterpouring

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OutlineOutline

1. Introduction2. The structure of distributed signals and sampling3. Distributed source coding

IntroductionSource coding, sampling, and Slepian-Wolf Distributed rate-distortion function for acoustic fields

4. On the interaction of source and channel coding5. Environmental monitoring6. Conclusions

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3.1 Correlated source coding and transmission3.1 Correlated source coding and transmission

Dense sources = correlated sources– physical world (e.g. landscape, room)– degrees of freedom ‘’limited’’– denser sampling: more correlated sources

Background: – Slepian- Wolf (lossless correlated source coding with binning)– Wyner-Ziv (source coding with side information)

Implications on communications– such results are starting to be used...– many open problems (general lossy case is still an open problem...)– separation might not be the way...– are there limiting results?

Below, specific results:– Distributed rate-distortion for acoustic fields based on plenacoustic

function– Also: Distributed compression: a distributed Karhunen-Loeve transform

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SlepianSlepian--WoldWold (1973(1973……))

Given– X, Y i.i.d with p(x,y)

Then: encode separately, decode jointly, without coders communicating

Achievable rate region

– R1 ¸ H(X/Y)– R2 ¸ H(Y/X)– R1 + R2 ¸ H(X,Y)

• For many sources…. rather complex (binning)• Lossy case: mostly open!• Example of result: SW based data gathering [CristescuBV:03]

R1

R2

H(X)

H(Y)

H(X/Y)

H(Y/X)

X

Y

R

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The The plenacousticplenacoustic function as a model, Konsbruck (1/3)function as a model, Konsbruck (1/3)

Stationary spatio-temporal source on a line, measured by a microphone array

Greens’ function

– FT essentially supported on a triangle!

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The The plenacousticplenacoustic function as a model (2/3)function as a model (2/3)

Quincunx sampling lattice

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The The plenacousticplenacoustic function as a model (3/3)function as a model (3/3)

Distributed rate-distortion functions– Centralized– Quincunx sampling based– Rectangular sampling based

Thus: the distributed R(D) is determined for this case!

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On distributed source codingOn distributed source coding……

Three cases studied:– Data gathering with Slepian-Wolf (Cristescu et al)– Distributed versions of the KLT (Gastpar et al)– Distributed rate-distortion for acoustic fields (above)

These are difficult problems....– lossy distributed compression partly open– high rate case: Quantization + Slepian-Wolf– low rate case: more open

In many case– Strong interaction of “source” and ‘’channel’’– Large gains possible

but we are only seeing the beginning of fully taking advantageof the sources structures and the communication medium...

Spring 2008 - 36

OutlineOutline

1. Introduction2. The structure of distributed signals and sampling3. Distributed source coding4. On the interaction of source and channel coding

To separate or not to separate...The world is analog, why go digital? To code or not to code...Gaussian sensor networks

5. Environmental monitoring6. Conclusions

Spring 2008 - 37

4. On the interaction of source and channel coding4. On the interaction of source and channel coding

Going digital is tightly linked to the separation principle:– in the point to point case, separation allows to use

“bits” as a universal currency

– but this is a miracle! (or a lucky coincidence)There is no reason that in multipoint source-channel transmission

the same currency will hold (M.Gastpar)Multi-source, multi-sink case:

– correlated source coding– uncoded transmission can be optimal– source-channel coding for sensor networks

Spring 2008 - 38

4.1 To separate or not to separate4.1 To separate or not to separate……

In point to point, if R < C, all is well in Shannon land. In multipoint communication, things are trickier (or more interesting)

Famous textbook counter example (e.g. Cover-Thomas)

No intersection, but communication possible!

R1

R2

H(X)

H(Y)

H(X/Y)

H(Y/X)

1/3 1/3

0 1/3

Y

X

log23

log23

Source

C1

C2 Channel

1

1

binary erasure multiaccess

Spring 2008 - 39

Sensor networks and source channel codingSensor networks and source channel coding

[GastparV:03/04]Consider the problem of sensing– one source of analog information but many sensors– reconstruct an estimate at the base station

Model: The CEO problem [Berger et al], Gaussian case

Question: distributed source compression and MIMO transmission oruncoded transmission?

Source

W1

W2

WM

U1

U2

UM

X1

X2

XM

F1

F2

FM

GS SY

Z

Spring 2008 - 40

Example: Gaussian Source, Gaussian NoiseExample: Gaussian Source, Gaussian Noise

Performance (cst or poly. growing power shared among sensors):– with uncoded transmission: – with separation:

Exponential suboptimality!

Condition for optimality: measure matching!

––– Can be generalized to many sources

Spring 2008 - 41

It is the best one can do:It is the best one can do:

Communication between sensors does not help as M grows!Intriguing remark:

– by going to ‘’bits’’, MSE went from 1/M to 1/Log(M)– ‘’bits’’ might not be a good idea for distributed sensing and

communicationsIf not ‘’bits’’, what is information in networks? [Gastpar:02]

Spring 2008 - 42

OutlineOutline

1. Introduction2. The structure of distributed signals and sampling3. Distributed source coding4. On the interaction of source and channel coding5. Environmental monitoring

monitoring for scientific purposesmonitoring for intelligent buildingsEnvironmental monitoringThe SensorScope project

6. Conclusions

Spring 2008 - 43

5. The case for environmental monitoring5. The case for environmental monitoring

(MICS applications)

5.1 Monitoring for scientific purposes– ‘’create’’ a new instrument for critical data– most current acquisitions are undersampled– verification of theory, simulations

Environmental data– unstable terrain, glaciers– watershed monitoring– pollutant monitoring, forest monitoring

University of Basel canopysensing and actuating

– Example: UCLA CENS. environmental monitoring

Spring 2008 - 44

5.2 The 5.2 The SensorScopeSensorScope Project (2005Project (2005--……))

(G. Barrenetxea, H.Dubois-Ferriere,T.Schmid,F.Ingelrest, G.Schaeffer) http://sensorscope.epfl.ch

What are we trying to accomplish?

SensorScope:– distributed sensing instrument – relevant datasets with clear documentation– all data on-line, real-time– anybody can compute/analyze with

Sensor nodes:– many possible platforms inc. low power (Berkeley motes, tinynode, tmote)– many types of sensing (e.g. cyclops)

First Step (SensorScope I):– a few dozen nodes– self-organized network up for 9 months– large dataset collects– fun platform and testbed

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The first networkThe first network

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The first network!The first network!

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Basic architectureBasic architecture

Sensor network with ad hoc data gathering protocols (10 to 100’s)Basestation with available wide area communication (e.g. GPRS)Web server with data onlineOpen source code and hardware

Spring 2008 - 48

SensorScopeSensorScope II and III [w. II and III [w. M.ParlangeM.Parlange]]Next step: SensorScope II and III

– collaboration with EFLUM (Laboratory of Environmental Fluid Mechanics and Hydrology)

– objective: gather environmental data for modeling of energy fluxes at earth-atmosphere boundary

– two large-scale outdoor sensor networking deployments: EPFL campus and alpine glacier

– very interesting theoretical (physics) and practical problems!– we need reliable and meaningful data!

Improved networking– packet combining, routing without routes– more power efficient platforms (tinynodes)

Data analysis– signals are far from....Gaussian!

Spring 2008 - 49

The core of The core of SensorScopeSensorScope: : WeatherStationWeatherStation

WeatherStation– centered around Tinynode (lowest-power sensor node)– solar energy subsystem: energy autonomous– water proof housing– seven external sensors measuring:

• temperature (ambient and surface)• humidity• wind speed and direction• soil moisture• solar radiation• precipitation

Solar energy system First Prototype WeatherStation

Spring 2008 - 50

Power is the basic problem!Power is the basic problem!

Communications is power hungryCareful management of powerPower gathering (e.g. solar panels)Energy efficient protocols for data gathering and GPRS connection

Power usage in a Tinynode(a) Off(b) Listening(c)-(g) various sending power

Spring 2008 - 51

WeatherStationWeatherStation Deployment at EPFL and Web InterfaceDeployment at EPFL and Web Interface

Objective:• Relevant datasets with

clear documentation• All data on-line:

http://sensorscope.epfl.ch/

• Anybody can compute/analyze with

Results• 9 months deployment in

2006• Microclimatic analysis

Spring 2008 - 52

GoingGoing to the to the mountainsmountains!!

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Real deployments: Grand Saint BernardReal deployments: Grand Saint Bernard

Real problem: multihop, changing topology, weather conditions• All data on-line: http://sensorbox.epfl.ch/main/

Spring 2008 - 54

Real deployments: Grand Saint BernardReal deployments: Grand Saint Bernard

Networks conditionsDriftPower consumptionCode updatesHardware failures

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Real Real deployementsdeployements: : GenepiGenepi

Real problem: land slides, infrastructure damage etc:

Understanding the changing environment, effects of warming, loss of permafrost etc

Spring 2008 - 56

Real Real deployementsdeployements: : GenepiGenepi

Location: Rock glacier above Martini (VS)

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Real Real deployementsdeployements

Latest pictures:• Fully autonomous camera (look, no wires!)• GPRS based• Onboard image processing• Open platform, linux based (Greenphone)

Spring 2008 - 60

A day in the life of A day in the life of GenepiGenepi!!

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Results from Results from GenepiGenepi

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Results from Results from GenepiGenepi

QuickTime™ and aNone decompressor

are needed to see this picture.

Spring 2008 - 63

Results: Monitoring the Results: Monitoring the PatrouillePatrouille des Glaciersdes Glaciers

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6. Conclusions6. Conclusions

There are some good questions on the interaction of– physics of the process: space of possible values– sensing: analog/digital– representation & compression: local/global– transmission: separate/joint– decoding & reconstruction: applications

From joint source-channel coding to source-channel communication– This goes back to Shannon’s original question,

but multi-source multi-point communication is hard...On-going basic questions:

– are there some fundamental bounds on certain data sets?– are there practical schemes to approach the bounds?– what is observable and what is not?

Applications:– environmental monitoring has many interesting,

high impact questions– technology amazingly mature– datasets very far from ‘’usual’’ models

Spring 2008 - 65

Outlook: The Swiss ExperimentOutlook: The Swiss Experiment

The Competence Center in Environment of the ETH domainlaunched a call for proposal

The SwissExperiment (EPFL, ETHZ, WSL) aims at generalizing the SensorScope platform for a number of experiments across the alps

It is an extraordinary project!– Scale– Locations– Team

• Totally new approach of doing environmental research– IT meets environment engineering– E-science: Open access

• How can we understand and predict environmental change– Not only science, also risk management

• It combines expertise, puts together a unique team– Consortium that combines academic institutions, federal offices,

industry, scientific experts and politicians, IT engineers and the public– Inter-institutional, interdisciplinary, international

Spring 2008 - 66

Thank you for your attention! Questions?Thank you for your attention! Questions?

© New Yorker

Spring 2008 - 67

ReferencesReferences

• On the NCCR-MICS: http://www.mics.org: all papers on line• On sensor networks and separation

– M.Gastpar, M.Vetterli, PL Dragotti, Sensing reality and communicating bits: A dangerous liaison - Is digital communication sufficient for sensor networks? IEEE Signal Processing Mag.,July 2006

• On sampling– M. Vetterli, P. Marziliano, T. Blu. Sampling signals with finite rate of innovation.

IEEE Tr. on SP, Jun. 2002.– T. Ajdler, L. Sbaiz and M. Vetterli, The plenacoustic function and its

sampling, IEEE Transactions on Signal Processing, Oct. 2006. – T. Ajdler, L. Sbaiz, A. Ridolfi and M. Vetterli, On a stochastic version of

the plenacoustic function, ICASSP06.• Correlated source coding

– R.Cristescu, B.Beferull and M.Vetterli, Correlated data gathering, Infocom2004.– M. Gastpar, P. L. Dragotti, and M. Vetterli. The distributed Karhunen-Loeve

transform. IEEE Tr. on IT, Dec. 06.– R.Konsbruck, E.Telatar, M.Vetterli, The distributed rate-distortion function of

sounds fields, ICASSP06

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ReferencesReferences

• Uncoded transmission, relays, and sensor networks– M. Gastpar, B. Rimoldi, M. Vetterli. To code or not to code: lossy source-channel

communication revisited, IEEE Tr. on IT, 2003– M.Gastpar, M..Vetterli, The capacity of large Gaussian relay networks, IEEE Tr

on IT, March 2005.• Flow Tomography

– I.Jovanovic, L.Sbaiz, M.Vetterli, Acoustic Flow Tomography, ICASSP06.• SensorScope

– See http://sensorscope.epfl.ch

Spring 2008 - 69

To code or not to code [GastparRV:03]:To code or not to code [GastparRV:03]:

Uncoded transmission for lossy source-channel communication

It is well known that a Gaussian source over a AWGN channelcan be ‘’sent as is’’, achieving optimal performance– easy way to achieve best performance (no delay...)

The parameters of source-channel coding are:– source distribution: – source distortion or error measure: – channel conditional distribution: – channel input cost function:

The art is measure matching!– D(R): channel has to look like the test channel to the source– C(P): source has to look like a capacity achiev. distrib. to the channel– in the Gaussian case, it all matches up! (MSE, power, densities)