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.
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.
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.