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Wireless Integrated Network
Sensors (WINS) : A Border security
             solution


                   Prepared By....
            Deepak Kumar Mohapatra
              Pijush Kumar Banerjee
OVERVIEW…
•   WINS Introduction
•   Evolution
•   Working Principle
•   System Architecture
•   Node Architecture
•   Micro sensor
•   Digital Signal Processing
•   Spectrum Analysis
•   Characteristics & applications
•   Reference
•   Conclusion
ABSRACT…
• Provides a new monitoring for the
  borders of the country.

• Easy identification of strangers or
  some terrorists entering the
  border area.

• Border area is partitioned into no.
  of mutually connected nodes.

• Noise detection, Image
  processing, Biometric
  scanning, Thermal
  detection, Density
  comparison, AV modulation etc.
  are the main key roles.
INTRO…
• WIRE LESS INTEGRATED NETWORK SENSOR (WINS).
• Require a few microwatt of power to operate, so it is
  cheaper than the conventional radar system.
• It produce a less amount delay to detect the target and
  reasonably faster.
Evolution Of WINS…
 WINS Initiated in 1993 under
  Defence advance research
  project agency (DARPA) in US.

 LWIM (Low power wireless
  integrated micro sensor) program
  began in 1995.

 In 1998, WINS introduced for
  wide variety of applications such
  as multihop, self-
  assembled, wireless network
  algorithms for operating at
  micropower levels.
Distributed sensor at Border…
Working Principle…
WINS System Architecture…
• WINS architecture
  includes sensor, data
  converter, signal
  processing, and control
  functions.

• The micro power
  components operate
  continuously for event
  recognition, while the
  network interface
  operates at low duty
                            Continuous operation   low duty cycle
  cycle.
WINS Node Architecture…
1998: WINS 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 => alert process to identify the event
      Further processing? Alert remote user / neighboring node?
      Communication between WINS nodes


    sensor                  signal processing for        Processing
                                                                               wireless
                               event detection
                                                    event classification &     internet
                                                       identification         interface
   actuator                        control



              continuously vigilant operation               low-duty cycle operation
Cont…
 WINS nodes are distributed at high density in an
  environment to be monitored.
 WINS node data is transferred over the asymmetric
  wireless link to an end user.
Nodes connection of WINS…
WINS MICRO SENSORS…
 The detector shown is the thermal detector. It just captures the
  harmonic signals produced by the footsteps of the stranger
  entering the border. Whole area is partitioned into hexagonal
  region.
 These signals are then converted into their PSD values and are
  then compared with the reference values set by the user.
Detection Procedure...
Micro Sensor and Thermal
Conductivity…




                           TC v/s Pressure
                           characteristics
Operation...
Sensor boards…




(a) Mica network Board
(b) Mica Sensor Board
(c) Mica Power Board
(d) TWR-ISM-002 Radar Board
(e) All of the boards attached
together.
                                 Packaging
Assembled Sensor Boards…
WINS DSP…
 If a stranger enters the border, his foot-steps will generate harmonic
  signals. It can be detected as a characteristic feature in a signal power
  spectrum. Thus, a spectrum analyzer must be implemented in the WINS.

 The spectrum analyzer resolves the WINS input data into a low-resolution
  power spectrum.




            WINS micropower spectrum analyzer architecture
Spectrum Analysis…
WINS Characteristics & Applications…

 Characteristics:
Support large numbers of sensor.
Dense sensor distributions .
These sensor are also developed to support
 short distance RF communication.
Internet access to sensors, controls and
 processor.
Cont…
 Applications:
• On a global scale, WINS will permit monitoring of
  land, water, and air resources for environmental
  monitoring .
• On a national scale, transportation systems, and
  borders will be monitored for
  efficiency, safety, and security.
• On a local, enterprise scale, WINS will create a
  manufacturing information service for cost and
  quality control.
Unanticipated Faulty Behavior…
• We experienced
  several failure as a
  result of undetectable,
  incorrectly download
  program and depleted
  energy level etc.

• For example node will
  detect false event
  when sensor board is
  overheated.
Reference…
 http://en.wikipedia.org/wiki/WINS

 http://en.wikipedia.org/wiki/Micro_sensors

 M. J. Dong, G. Yung, and W. J. Kaiser, “Low Power Signal
  Processing Architectures for Network Microsensors”, 1997
  International Symposium on Low Power Electronics and
  Design, Digest of Technical Papers (1997).

 A. A. Abidi, “Low-power radio-frequency ICs for portable
  communications”, Proceedings of the IEEE, 83, (1995).

 D. B. Leeson, “A simple model of feedback oscillator noise
  spectra”.
Conclusion…
 Densely distributed
 sensor networks.
 Application specific
 networking architectures
 Development platforms
 are now available .
 The network is self-
 monitoring and secure.
 Now it is possible to
 secure the border with an
 invisible wall of thousands
 or even millions of tiny
 interconnected sensors.
WIRELWIRELESS INTEGRATED NETWORK SENSORS

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WIRELWIRELESS INTEGRATED NETWORK SENSORS

  • 1. Wireless Integrated Network Sensors (WINS) : A Border security solution Prepared By.... Deepak Kumar Mohapatra Pijush Kumar Banerjee
  • 2. OVERVIEW… • WINS Introduction • Evolution • Working Principle • System Architecture • Node Architecture • Micro sensor • Digital Signal Processing • Spectrum Analysis • Characteristics & applications • Reference • Conclusion
  • 3. ABSRACT… • Provides a new monitoring for the borders of the country. • Easy identification of strangers or some terrorists entering the border area. • Border area is partitioned into no. of mutually connected nodes. • Noise detection, Image processing, Biometric scanning, Thermal detection, Density comparison, AV modulation etc. are the main key roles.
  • 4. INTRO… • WIRE LESS INTEGRATED NETWORK SENSOR (WINS). • Require a few microwatt of power to operate, so it is cheaper than the conventional radar system. • It produce a less amount delay to detect the target and reasonably faster.
  • 5. Evolution Of WINS…  WINS Initiated in 1993 under Defence advance research project agency (DARPA) in US.  LWIM (Low power wireless integrated micro sensor) program began in 1995.  In 1998, WINS introduced for wide variety of applications such as multihop, self- assembled, wireless network algorithms for operating at micropower levels.
  • 8. WINS System Architecture… • WINS architecture includes sensor, data converter, signal processing, and control functions. • The micro power components operate continuously for event recognition, while the network interface operates at low duty Continuous operation low duty cycle cycle.
  • 9. WINS Node Architecture… 1998: WINS 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 => alert process to identify the event  Further processing? Alert remote user / neighboring node?  Communication between WINS nodes sensor signal processing for Processing wireless event detection event classification & internet identification interface actuator control continuously vigilant operation low-duty cycle operation
  • 10. Cont…  WINS nodes are distributed at high density in an environment to be monitored.  WINS node data is transferred over the asymmetric wireless link to an end user.
  • 11.
  • 13. WINS MICRO SENSORS…  The detector shown is the thermal detector. It just captures the harmonic signals produced by the footsteps of the stranger entering the border. Whole area is partitioned into hexagonal region.  These signals are then converted into their PSD values and are then compared with the reference values set by the user.
  • 15. Micro Sensor and Thermal Conductivity… TC v/s Pressure characteristics
  • 17. Sensor boards… (a) Mica network Board (b) Mica Sensor Board (c) Mica Power Board (d) TWR-ISM-002 Radar Board (e) All of the boards attached together. Packaging
  • 19. WINS DSP…  If a stranger enters the border, his foot-steps will generate harmonic signals. It can be detected as a characteristic feature in a signal power spectrum. Thus, a spectrum analyzer must be implemented in the WINS.  The spectrum analyzer resolves the WINS input data into a low-resolution power spectrum. WINS micropower spectrum analyzer architecture
  • 21. WINS Characteristics & Applications…  Characteristics: Support large numbers of sensor. Dense sensor distributions . These sensor are also developed to support short distance RF communication. Internet access to sensors, controls and processor.
  • 22. Cont…  Applications: • On a global scale, WINS will permit monitoring of land, water, and air resources for environmental monitoring . • On a national scale, transportation systems, and borders will be monitored for efficiency, safety, and security. • On a local, enterprise scale, WINS will create a manufacturing information service for cost and quality control.
  • 23. Unanticipated Faulty Behavior… • We experienced several failure as a result of undetectable, incorrectly download program and depleted energy level etc. • For example node will detect false event when sensor board is overheated.
  • 24. Reference…  http://en.wikipedia.org/wiki/WINS  http://en.wikipedia.org/wiki/Micro_sensors  M. J. Dong, G. Yung, and W. J. Kaiser, “Low Power Signal Processing Architectures for Network Microsensors”, 1997 International Symposium on Low Power Electronics and Design, Digest of Technical Papers (1997).  A. A. Abidi, “Low-power radio-frequency ICs for portable communications”, Proceedings of the IEEE, 83, (1995).  D. B. Leeson, “A simple model of feedback oscillator noise spectra”.
  • 25. Conclusion…  Densely distributed sensor networks.  Application specific networking architectures  Development platforms are now available .  The network is self- monitoring and secure.  Now it is possible to secure the border with an invisible wall of thousands or even millions of tiny interconnected sensors.