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Title: wireless integrated n/w sensors


1
Wireless Integrated Network Sensors
  • Barbara Theodorides
  • April 15, 2003

2
Paper
  • G. J. Pottie and W. J. Kaiser, Wireless
    Integrated Network Sensors, Communications of
    ACM, 43(5), May 2000.

3
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.

4
A general picture
worldwide user
local area
low power networking
Internet
sensing
wireless communication
signal processing / event recognition
5
Concerned about
  • The Physical principles ?dense sensor network
  • Energy bandwidth constraints ?distributed
    layered signal
    processing architecture
  • WINS network architecture
  • WINS nodes architecture

6
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
7
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
8
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

9
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.
10
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
11
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
12
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

13
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

14
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

15
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

16
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

17
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

18
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

19
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
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