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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
Jan. 28, 2004 UCB Sensor Nets Day
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)
Jan. 28, 2004 UCB Sensor Nets Day
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:
Jan. 28, 2004 UCB Sensor Nets Day
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
Jan. 28, 2004 UCB Sensor Nets Day
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
Jan. 28, 2004 UCB Sensor Nets Day
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?
Jan. 28, 2004 UCB Sensor Nets Day
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)
Jan. 28, 2004 UCB Sensor Nets Day
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)
Jan. 28, 2004 UCB Sensor Nets Day
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.
Jan. 28, 2004 UCB Sensor Nets Day
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
Jan. 28, 2004 UCB Sensor Nets Day
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
Jan. 28, 2004 UCB Sensor Nets Day
ResultResult
• If collaborative processing is (MSE) optimal when R is infinity, …
22 ||ˆ||||~
|| XXEXXE • Here, R = infinity and
Jan. 28, 2004 UCB Sensor Nets Day
• … 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
Jan. 28, 2004 UCB Sensor Nets Day
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
Jan. 28, 2004 UCB Sensor Nets Day
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
Jan. 28, 2004 UCB Sensor Nets Day
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.
Jan. 28, 2004 UCB Sensor Nets Day
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)
Jan. 28, 2004 UCB Sensor Nets Day
• 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
Jan. 28, 2004 UCB Sensor Nets Day
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
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