13-09-2014, 11:20 AM
BORDER SECURITY USING WIRELESS INTEGRATED NETWORK SENSOR
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ABSTRACT
Wireless Integrated Network Sensors (WINS) now provide a new
monitoring and control capability for monitoring the borders of the
country. Using this concept we can easily identify a stranger or some
terrorists entering the border. The border area is divided into number of
nodes. Each node is in contact with each other and with the main node. The
noise produced by the foot-steps of the stranger are collected using the
sensor. This sensed signal is then converted into power spectral density and
the compared with reference value of our convenience. Accordingly the
compared value is processed using a microprocessor, which sends
appropriate signals to the main node. Thus the stranger is identified at the
main node. A series of interface, signal processing, and communication
systems have been implemented in micro power CMOS circuits. A micro
power spectrum analyzer has been developed to enable low power
operation of the entire WINS system.Thus WINS require a Microwatt of
power. But it is very cheaper when compared to other security systems
such as RADAR under use. It is even used for short distance
communication less than 1 Km. It produces a less amount of delay
INTRODUCTION
Wireless Integrated Network Sensors (WINS) combine sensing, signal
processing, decision capability, and wireless networking capability in a compact low
power system. Compact geometry and low cost allows WINS to be embedded and
distributed at a small fraction of the cost of conventional wireline sensor and actuator
systems.
For example, 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, wide area scale, battle field situational awareness will provide
personal health monitoring and enhance security and efficiency. Also, on a
metropolitan scale, new traffic, security, emergency, and disaster recovery services
will be enabled by WINS. On a local, enterprise scale, WINS will create a
manufacturing information service for cost and quality control.
WINS SYSTEM ARCHITECTURE
The primary limitation on WINS node cost and volume arises from power
requirements and the need for battery energy sources. As will be described, low
power sensor interface and signal processing architecture and circuits enable
continuous low power monitoring. However, wireless communication energy
requirements present additional severe demands. Conventional wireless networks are
supported by complex protocols that are developed for voice and data transmission for
handhelds and mobile terminals.
Conventional wireless networks are supported by complex protocols that are
developed for voice and data transmission for handheld and mobile terminals. These
networks are also developed to support communication over long range (up to 1Km or
more) with link bit rate over 100Kbps. In contrast to wireless networks, the WINS
network support large number of sensors in a local area with short range and low
average bit rate communication (less than 1Kbps). The networks design must consider
the requirement to service dense sensor distributions with an emphasis on recovering
environment information. Multihop communication yields large power and scalability
advantage for WINS network. Multihop communication therefore provides an
immediate advance in capability for the WINS narrow Bandwidth device. The figure
1 represents the general structure of the wireless integrated network sensors (WINS)
arrangement.
WINS NODE ARCHITECTURE
The Wins node architecture (figure1) is developed to enable continuous
sensing, event detection, and event identification at low power. Since the event
detection process must occur continuously, the sensor, data converter, data buffer, and
spectrum analyser must all operate at micro power levels. In the event that an event is
detected, the spectrum analyser output may triggered the microcontroller may then
issue commands for additional signal processing operation for identification of the
event signal. Protocols for node operation then determine whether a remote user or
neighbouring WINS node should be alerted. The WINS node then supplies an
attribute of the identified event, for example, the address of the event in an event
look-up-table stored in all network nodes. Total average system supply currents must
be less than 30 A.
WINS MICROSENSORS
Source signals (seismic, infrared, acoustics and others) all decay in
mplitude rapidly with radial distance from the source. To maximize detection
range, sensor sensitivity must be optimized. In addition, due to the
fundamental limits of background noise, a maximum detection range exists for
any sensor. Thus, it is critical to obtain the greatest sensitivity and to develop
compact sensors that may be widely distributed. Clearly,
microelectromechanical systems (MEMS) technology provides an ideal path
for implementation of these highly distributed systems. The sensor-substrate
“sensortrate” is then a platform for support of interface, signal processing and
communication circuits. Examples of WINS Micro Seismometer and infrared
detector devices are shown in figure 3. The detector shown is the thermal
detector. It just captures the harmonic signals produced by the foot-steps of the
stranger entering the border. These signals are then covered into their PSD
values and are then compared with the reference values set by the user
Bonding pads
WINS MICROSENSOR INTERFACE CIRCUITS
The WINS microsensor systems must be monitored continuously by the
CMOS micropower analog-to-digital converter (ADC). As was noted above, power
requirements constrain the ADC design to power levels of 30W or less. Sensor
sample rate for typical microsensor applications is less than 1kHz (for example the
infrared microsensor bandwidth is 50Hz, thus limiting required sample rate to 100
Hz). Also, it is important to note that the signal frequency is low. Specifically, the
themopile infrared sensor may be employed to detect temperature, presence, of
motion at near dc signal frequencies. Therefore, the ADC must show high stability
(low input-referred noise at low frequency). For the WINS ADC application, a first
order Sigma-Delta (S-D) converter is chosen over other architectures due to power
constraints. The S-D architecture is also compatible with the limitations of low cost
digital CMOS technologies.
The analog components of the ADC operate in deep subthreshold to meet the
goal of micropower operation . This imposes severe bandwidth restrictions on the
performance of the circuits within the loop. A high oversampling ratio of 1024 is thus
chosen to overcome the problems associated with low performance circuits. The
possible increased power consumption of digital components in the signal path
including the low pass filter is minimized with the use of low power cell libraries and
architecture
WINS DIGITAL SIGNAL PROCESSING
The WINS architecture relies on a low power spectrum analyzer to process all
ADC output data to identify an event in the physical input signal time series. Typical
events for many applications generate harmonic signals that may be detected as a
characteristic feature in a signal power spectrum. Thus, a spectrum analyzer must be
implemented in the WINS digital signal processing system. The spectrum analyzer
resolves the WINS 8-bit ADC input data into a low resolution power spectrum. Power
spectral density (PSD) in each of 8 frequency “bins” is computed with adjustable
band location and width. Bandwidth and position for each power spectrum bin is
matched to the specific detection problem. Since this system must operate
continuously, as for the ADC, discussed above, the WINS spectrum analyzer must
operate at mW power level
WINS MICROPOWER EMBEDDED RADIO
WINS systems present novel requirements for low cost, low power, short
range, and low bit rate RF communication. In contrast to previous emphasis in
wireless networks for voice and data, distributed sensors and embedded
microcontrollers raise these new requirements while relaxing the requirements on
latencyand throughput. The WINS RF modem becomes an embedded radio with a
system that may be added to compact microdevices without significantly impacting
cost, form factor, or power. However, in contrast to previously developed simple, low
power RF modems, the WINS device must fully support networking. In addition, the
WINS radio should be compatible with compact packaging.
Communication and networking protocols for the embedded radio are now a
topic of research. However, simulation and experimental verification in the field
indicate that the embedded radio network must include spread spectrum signaling,
channel coding, and time division multiple access (TDMA) network protocols. The
operating bands for the embedded radio are most conveniently the unlicensed bands at
902-928 MHz and near 2.4 GHz. These bands provide a compromise between the
power cost associated with high frequency operation and the penalty in antenna gain
reduction with decreasing frequency for compact antennas
CONCLUSION
A series of interface, signal processing, and communication systems have
been implemented in micro power CMOS circuits. A micro power spectrum analyser
has been enabled to low power operation to the entire WINS system. Thus WINS
require a Microwatt of power. But it is very cheaper when compared to other security
systems such as RADAR under use. It is even under used for short distance
communication less than 1 Km. it produces a less amount of delay. Hence it is
reasonably faster. On a global scale, WINS will permit monitoring of land, water, and
air resources for environmental monitoring. On a national scale, transportation
system, and borders will be monitored for efficiency, safety, and security