Jan. 28, 2004UCB Sensor Nets Day The BASiCS Group Berkeley Audio-visual Signal processing and Communication Systems Kannan Ramchandran Distributed signal

Jan. 28, 2004 UCB Sensor Nets Day

Wireless FoundationsWireless Foundations

http://www.eecs.berkeley.edu/wireless/

A fundamental research core of UC Berkeley researchers to provide the theoretical and algorithmic foundations

for tomorrow’s wireless systems

Core faculty: V.Anantharam, M. Gastpar, A. Sahai,K. Ramchandran, D. Tse

The BASiCS GroupThe BASiCS GroupBerkeley Audio-visual Signal processing and Communication SystemsBerkeley Audio-visual Signal processing and Communication Systems

Kannan Ramchandran

Distributed signal processing: Distributed signal processing: compression: challenges and compression: challenges and

opportunitiesopportunities

http://www.basics.eecs.berkeley.edu

http://www.berkeley.edu/


Towards a System Theory for Robust Towards a System Theory for Robust Large-Scale Sensor NetworksLarge-Scale Sensor Networks

Closing the Loop: Inference & Adaptive Control

f(x)

Representation and Data-Acquisition:Distributed Sampling Theory

Design guidelines for robust large-scale networks:

Channel Physics, Percolation Theory

Information Dissemination: Routing, Compressing, Mobility

NSF Sensors (Ramchandran, Sastry, Tse, Vetterli, Poolla)


Sensor networks: a systems viewSensor networks: a systems view

• Data acquisition • Distributed compression and communication• Networking and routing• Distributed inference and decision (classification / estimation)• Closing the loop (control)

• Statistical models for sensor-fields

• Scaling laws for dense networks

• Information and coding theory

• Learning theory and adaptive signal processing

Systems tasks:

Guiding principles:


Distributed SP (DSP): “low-hanging fruit”Distributed SP (DSP): “low-hanging fruit”

Revisit many classical SP problems (estimation, inference, detection, fusion) under constraints of:

bandwidth (compression) noisy transmission medium (coding + MAC) total system energy (communication + processing) highly unreliable system components (robust design)

Voila you get a “distributed signal processing” recipe!•Constraints force robust distributed solutions – sampling, processing, routing, compressing, coding, controlling.

Architectures should reflect and exploit computational diversity in wireless devices (TV’s, cell phones, laptops, cheap sensors)

Asymmetric complexities

In-built robustness & fault-tolerant designs:

Diversity in representation & communication

Rehaul “deterministic” frameworks (e.g. prediction-based) with “probabilistic” ones


Sampling sensor fieldsSampling sensor fields

– Many physical signals e.g., pressure, temperature, are approximately BL

– Physical propagation laws often provide a natural smoothing effect

Sensor network constraints • Low-precision A/D

• Limited power and bandwidth

T 2T 3T time

space

Sampling a 1-D spatio-temporal field

A/D converters (sensors)

good

bad

good

“Central unit”

X

2XX

X2X 2X


Motivation: Acquisition & reconstruction of sensor fieldsMotivation: Acquisition & reconstruction of sensor fields

f(x)

Is there an “information” scaling law ?• [Gupta-Kumar’00]: In ad-hoc networks, with

independent data sources, throughput/sensor 0 as 1/sqrt(N).

• In sensor nets, data correlation increases with density. • Can information-rate/sensor and reconstruction distortion go to zero with density?

Tradeoffs between sensor precision and # of sensors?• Can we overcome low precision sensors by throwing scale at the problem?• Is there an underlying “conservation of bits” principle?


Sensor-Field Reconstruction: ‘Distributed’ Sampling TheorySensor-Field Reconstruction: ‘Distributed’ Sampling Theory • “Conservation of bits” principle We can trade off A/D precision

for oversampling rate (quality bits per Nyquist interval).

1 bit/sample, T/22 bits/sample, Tsimilar

accuracy

D = c 2-k

D

k Bit-budget

Err

or

0

(k,0)

(1,k)

A/D precision b-bitlog

(# o

f se

nsor

s)(k-1,2)

(k-2,3)

(2,k-1)

• Need concept of “dithering” and “distributed coding”Ishwar, Kumar & Ramchandran (IPSN ’03)

• Distortion ~ O(1/N)

• RNyquist ~ O(log N)

• Rsensor ~ O(log N / N)


Overcoming Unreliable RadiosOvercoming Unreliable Radios• Narrowband Radios

– Simple, used by all sensor nodes today [Motes, PicoRadio, Ember, SmartDust]

– How to get fcarrier?

• Crystal Oscillator (precise but expensive)• MEMS Resonator (less precise & less expensive)• On-chip LC-Resonator (cheap, low-power, imprecise)

fcarrier

P(f ca

rrie

r)

3 variation

Signal BW

•Can we overcome cheap radios by throwing scale at the problem?

• Can we devise clever probabilistic distributed algorithms for routing & network coding that exploit the randomness in the manufacturing process?

(Picoradio)


Distributed compressionDistributed compression

Encoder Decoder

XY

X^

•The encoder needs to compress the source X.•The decoder has access to correlated side information Y. •Can we compress X to H(X|Y)?

Information theory: X can be theoretically compressed at a rate equal to that when the encoder too has access to Y

XY

Dense, low-powersensor-networks

Can design practical distr. source coding framework to approach this.


Integrating learning: correlation trackingIntegrating learning: correlation tracking

• Many sensors report to controller• Correlation tracking

– Controller keeps track of correlation

– Specifies how much compression– Sensors blindly encode readings

• Minimal processing at sensor nodes– Complexity at controller– Cheap sensors

• Probabilistic reference to side information allows for robustness to packet loss

• • •

XSource

Channel • • •

R

R

R Collector


Collaborative processing: Collaborative processing: compressing raw-data versus local estimatescompressing raw-data versus local estimates

Several scenarios:• Sensor-clusters (groups of sensors that can collaborative)• Multiple antennas per sensor• Multimodal sensors


ResultResult

• If collaborative processing is (MSE) optimal when R is infinity, …

22 ||ˆ||||~

|| XXEXXE • Here, R = infinity and


• … then it is also optimal for any finite R.

Suggests that distributed estimation and compression tasks can be “de-coupled”, i.e., one can design & adapt network topology by

ignoring bandwidth requirements in a number of scenarios.

ResultResult


Opportunities: architecture rehaulsOpportunities: architecture rehauls

Architectures should reflect and exploit computational diversity in wireless devices (TV’s, cell phones, laptops, cheap sensors)

Asymmetric complexities

In-built robustness & fault-tolerant designs:

Diversity in representation & communication

Rehaul “deterministic” frameworks (e.g.prediction-based frameworks: LP, DPCM, etc.) with “probabilistic” ones


Rethinking video-over-wirelessRethinking video-over-wireless

Today’s video architectures shaped by downlink broadcast model:

Complex encoder

Light decoder

Motion estimation task dominates (up to 90%)

• Ultra-low-power video sensors and surveillance cameras• Multimedia-enabled cellphones & PDA’s • High-resolution wireless digital video cameras• Wireless-video teleconferencing systems• Home-entertainment and home-networking systems

Changing landscape: “uplink” heavy applications

Video is not just a downlink broadcast experience any more!

Wireless Network

Wireless Network


New class of video codecs: requirements

Light codec complexity in order to Maximize battery-life. Satisfy complexity constraints at encoding

device.

High compression efficiency to match Available bandwidth/storage constraints. Low transmission power constraints.

Robustness to packet/frame drops to Combat harsh wireless transmission medium.


Heavy encoder

Light decoder

EncoderDecoder

light

Transcoding proxy

Rethinking the division of laborRethinking the division of labor

Under reasonable signal models, it is possible to transfer (motion search) complexity to decoder without loss of compression efficiency (Ishwar, Prabhakaran, & Ramchandran, 2003)


• Sequence used: Football (14 frames, 352x240)

• Comparison: H.263+ (free version from UBC, Vancouver)

• Frame rate: 30fps, Encoding rate: 10kB per frame

• Compression: Performance is visually competitive with respect to full-motion complex inter-frame codecs such as MPEG-4 & H.263+.

(For pure compression, H.263+ outperforms PRISM by about 1.3 dB on our tests on the Football sequence)

• Robustness: Much more robust than current solutions. Can recover from frame losses. - Test for robustness: second frame was removed from frame

memory after decoding. third frame was decoded off the first frame in both cases.

Noerror.bat

Frame2.bat

PRISM video simulation results


Qualcomm’s simulator for “CDMA-2000 1X”Qualcomm’s simulator for “CDMA-2000 1X”

• At packet error rate 6%:

• At packet error rate 11%:

• H.263+ at packet error rate of 3% and PRISM at 16%:

PRISM is 4-8 dB better than H.263+ for the loss rates

investigated.

6percent.bat

11percent.bat

16vs3percent.bat

Documents

Jan. 28, 2004UCB Sensor Nets Day The BASiCS Group Berkeley Audio-visual Signal processing and Communication Systems Kannan Ramchandran Distributed signal