wireless integrated n/w sensors

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Wireless Integrated Network Sensors

• Barbara Theodorides
• April 15, 2003

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Paper

• G. J. Pottie and W. J. Kaiser, Wireless
  Integrated Network Sensors, Communications of
  ACM, 43(5), May 2000.

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WINS

• Initiated in 1993 at the UCLA, 1G fielded in 1996
• Sponsored by DARPA ? LWIM program began in 1995
• In 1998, WINS NG
• Distributed network
• Internet access to sensors, controls and
  processors
• Low-power signal processing, computation, and
  low-cost wireless networking
• RF communication over short distances ( lt 30m )
• Applications Industries, transportation,
  manufacture, health care, environmental
  oversight, and safety security.

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A general picture
worldwide user
local area
low power networking
Internet
sensing
wireless communication
signal processing / event recognition
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Concerned about

• The Physical principles ?dense sensor network
• Energy bandwidth constraints ?distributed
  layered signal
  processing architecture
• WINS network architecture
• WINS nodes architecture

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Physical Principles

• When are distributed sensors better?
• A. Propagation laws for sensing
• All signals decay with distance
• e.g. electromagnetic waves in
  free space ( 1/d2)
• in
  other media (absorption, scattering,
  
  dispersion)

distant sensor requires costly operations
If the system is to detect objects reliably, it
has to be distributed, whatever the networking
cost
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Physical Principles (cont)

• What are the fundamental limits driving the
  design of a network of distributed sensors?
• B. Detection Estimation
• Detector given a set of observables xj
• determines which of the hypotheses hi are
  true
• Target presence/absence based on estimates
  parameters fk of xj
• Selected Fourier, wavelet transform coefficients
• Marginal improvement
• Formally Decide on hi if p(hi fk) gt p(hj
  fk) ? j ? i
• Reliability independent
  observations, SNR
• Complexity dimension of feature space,
  hypotheses

Either a longer set of independent observations
or high SNR
Decrease the features and the hypotheses
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Physical Principles (cont)

• Use of practical Algorithms
• Apply deconvolution and target-separation
  machinery to exploit a distributed array (deal
  with only 1 target and no propagation dispersal
  effects)
• - reduces feature space hypotheses
• cons complexity
• Deploy a dense sensor network
• - homogeneous environment within the
  detection range
• - reduces environmental features ?size of
  decision space
• attractive method

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Physical Principles (cont)

• C. Communication Constraints
• Spatial separation (e.g. low lying antennas)
• Surface roughness, reflecting obstructing
  objects
• However ? spatial isolation, reuse of
  frequencies
• Multipath propagation (reflections off multiple
  objects)
• Recover space, frequency, and time diversity
  
• But ? for static nodes, time diversity is
  not an option
• ? spatial diversity is difficult
  to obtain
• Diversity in frequency domain
• Shadowing dealt with by employing a multihop
  network

The greater the density, the closer the nodes,
and the greater the likelihood of having a link
with sufficiently small distance and shadowing
losses.
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Physical Principles (cont)

• D. Energy Consumption
• Limits to the energy efficiency of CMOS
  communications and signal-processing circuits
• Limits on the power required to transmit reliably
  over a given distance

Networks should be designed so that radio is off
as much of the time as possible and otherwise
transmits only at the minimum required level

• ASICs can clock at much lower speeds ?
  consume less energy

ASICs maintain a cost advantage
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Signal-Processing Architecture

• We want low false-alarm high detection
  probability
• Processing Hierarchy

Human
Sophisticated Methods
Collaboration of WINS nodes
Higher-energy processing sensing
Energy thresholding
Precision Cost
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Signal-Processing Architecture (cont)

• Application Specific
• e.g. Remote security application
• WINS node 2 sensors (seismic imaging
  capability)
• Seismic senor requires little power ? constantly
  vigilant
• Simple energy detection triggers the cameras
  operation
• Collaborative WINS nodes (e.g. target location)
• Send image seismic record to a remote observer
• WINS node simple processing at low power
• Radio does not need to support continuous
  transmission of images

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WINS Network Architecture

• Characteristics
• Support large numbers of sensor
• Low average bit rate communication ( lt 1-100 Kbps
  )
• Dense sensor distributions
• Exploit the short-distance separation ?multihop
  communication
• Protocols designed so radios are off ? MAC
  address should include some variant of
  time-division access
• Time-division protocol
• Exchange small messages performance information,
  synchronization,
• bandwidth reservation requests
• Abundant bandwidth ? few conflicts, simple
  mechanisms
• At least one low-power protocol suite has been
  developed ? feasible to achieve distributed
  low-power operation in a flat multihop network

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WINS Network Architecture (cont)

• Link Sensor Network to the Internet
• Layering of the protocols (and devices) is needed
• WINS Gateways Support for the WINS network and
  access between conventional network physical
  layers and their protocols and between the WINS
  physical layer and its low-power protocols
• System Architect Responsibilities
• Applications requirements (reduced operation
  power, improved bit rate, improved bit error
  rate, reduced cost)
• How can Internet protocols (TCP, IPv6) be
  employed?
• - need to conserve energy, unreliability of
  physical channels
• Where should the processing and the storage take
  place?
• - at the source / reducing the amount of data to
  transmit

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WINS Node Architecture

• 1993 Initiated at the UCLA
• 1G of field-ready WINS devices and
  software was fielded (1996)
• 1995 DARPA sponsored
• - the LWIM project ? multihop,
  self-assembled, wireless network
• algorithms for operating at micropower
  levels
• - the joint, UCLA and Rockwell Science
  Center of Thousand Oaks,
• program ? platform for more sophisticated
  networking and signal processing algorithms
  (many types of sensors, less emphasis on
  power conservation)
• Lesson Separate real-time from higher-level
  functions

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WINS Node Architecture (cont)

• 1998 WINS NG developed by the authors ?
  contiguous sensing, signal processing for event
  detection, local control of actuators, event
  classification, communication at low power
• Event detection is contiguous ? micropower levels
• Event detected gt alert process to identify the
  event
• Further processing? Alert remote user /
  neighboring node?
• Communication between WINS nodes

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WINS Node Architecture (cont)

• Further Generations (Future work)
• Support plug-in Linux devices
• Small, limited sensing devices ? interact with
  WINS NG nodes in heterogeneous networks
• Scavenge energy from the environment ?
  photocells

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Why WINS ?

• Low power consumption ( 100 µW average )
• Separation of real-time from higher level
  functions
• Hierarchical signal-processing architecture
• Application specific
• Communication facility ( WINS gateways )
• Remote user
• Scalable
• Reduce amount of data to be send ? scalability to
  thousands of nodes per gateway

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Conclusion

• Densely distributed sensor networks (physical
  constraints)
• Layered and heterogeneous processing
• Application specific networking architectures
• Close intertwining of network processing
• Development platforms are now available