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

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http//www.basics.eecs.berkeley.edu
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Towards a System Theory for Robust Large-Scale
Sensor Networks
NSF Sensors (Ramchandran, Sastry, Tse, Vetterli,
Poolla)
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Sensor networks a systems view
Systems tasks

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

Guiding principles

• Statistical models for sensor-fields
• Scaling laws for dense networks
• Information and coding theory
• Learning theory and adaptive signal processing

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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
  (TVs, 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

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Sampling sensor fields

• Many physical signals e.g., pressure,
  temperature, are approximately BL
• Physical propagation laws often provide a
  natural smoothing effect

A/D converters (sensors)

• Sensor network constraints
• Low-precision A/D
• Limited power and bandwidth

Sampling a 1-D spatio-temporal field
space
2X
2X
2X
X
X
X
T
2T
3T
time
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Motivation Acquisition reconstruction of
sensor fields

• Is there an information scaling law ?
• Gupta-Kumar00 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?

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Sensor-Field Reconstruction Distributed
Sampling Theory

• Conservation of bits principle We can trade
  off A/D precision for oversampling rate (quality
  ? bits per Nyquist interval).

0
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Overcoming 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)

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Distributed compression
Dense, low-power sensor-networks

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

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

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Collaborative processing compressing raw-data
versus local estimates

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

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Result

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

• Here, R infinity and

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Result

• 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.
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Opportunities architecture rehauls

• Architectures should reflect and exploit
  computational diversity in wireless devices
  (TVs, 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

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Rethinking video-over-wireless

• Todays video architectures shaped by downlink
  broadcast model
• Complex encoder
• Light decoder

Motion estimation task dominates (up to 90)
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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.

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Rethinking 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)
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PRISM video simulation results

• 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.

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