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. BORDER SECURITY FORCE USING
WIRELESS INTEGRATED NETWORK
SENSOR
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CONTENTS
ABSTRACT
Chapter 1: INTRODUCTION
1.1 Distributed Sensor at Border
Chapter 2: WINS ARCHIKTECTURE
2.1 Wins System Architecture
2.2 Wins Node Architecture
2.3 Wins Digital Signal Processing
2.4 Wins Micro Sensors
Chapter 3: ROUTING BETWEEN NODES
3.1 Shortest Distance Algorithm
Chapter 4: PSD COMPARISION
4.1 Wins Micropower Embedded Ratio
Chapter 5: DESIGN CONSIDERATION
Chapter 6: ADVANTAGES, DISADVANTAGES &
APPLICATIONS
Chapter 7: Conclusion
REFERENCE
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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 beendeveloped 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. 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 systems, and borders will be
monitored for efficiency, safety, and security.
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Chapter 1:
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. On a local, wide-
area scale, battlefield situational awareness will provide personnel 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. The
opportunities for WINS depend on the development of scalable, low cost, sensor network
architecture. This requires that sensor information be conveyed to the user at low bit rate with
low power transceivers. Continuous sensor signal processing must be provided to enable
constant monitoring of events in an environment. Distributed signal processing and decision
making enable events to be identified at the remote sensor. Thus, information in the form of
decisions is conveyed in short message packets. Future applications of distributed embedded
processors and sensors will require massive numbers of devices. In this paper we have
concentrated in the most important application, Border Security.
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1.1 Distributed sensor at Border
Figure shows the distribution of sensors at the border of nation. Different sensors are
connected together and also connected to the Gateway. The information sensed at the
sen so rs i s co mmu n ica ted to g a tewa y . T h e sen so rs a re d i s t r ib u ted o n
gr o u nd , a i r an d inside the water also. All these sensors are connected using Wireless
Network.
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Chapter 2:
WINS ARCHIKTECTURE2.1 WINS SYSTEM ARCHITECTURE
Conventional wireless networks are supported by complex protocols that are
developed for voice and data transmission for handhelds 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 conventional wireless networks, the WINS
network must support large numbers of sensors in a local area with short range and low
average bit rate communication (less than 1kbps). The network design must consider the
requirement to service dense sensor distributions with an emphasis on recovering
environment information. Multihop communication yields large power and scalability
advantages for WINS networks. Multihop communication, therefore, provides an immediate
advance in capability for the WINS narrow Bandwidth devices. However, WINS Multihop
Communication networks permit large power reduction and the implementation of dense
node distribution. The multihop communication has been shown in the figure 2. The figure 1
represents the general structure of the wireless integrated network sensors (WINS)
arrangement.
Continuous operation low duty cycle
Figure 1. The wireless integrated network sensor (WINS) architecture.
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2.2 WINS NODE ARCHITECTURE
The WINS node architecture (Figure 1) 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 analyzer must all
operate at micro power levels. In the event that an event is detected, the spectrum analyzer
output may trigger the microcontroller. The microcontroller may then issue commands for
additional signal processing operations for identification of the event signal. Protocols for
node operation then determine whether a remote user or neighboring 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 30A. Low power, reliable, and efficient networkoperation is obtained with intelligent sensor nodes that include sensor signal processing,
control, and a wireless network interface. Distributed network sensor devices must
continuously monitor multiple sensor systems, process sensor signals, and adapt to changing
environments and user requirements, while completing decisions on measured signals.
Figure 2. WINS nodes (shown as disks)
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For the particular applications of military security, the WINS sensor systems must operate at
low power, sampling at low frequency and with environmental background limited
sensitivity. The micro power interface circuits must sample at dc or low frequency where
1/f noise in these CMOS interfaces is large. The micropower signalprocessing system must
be implemented at low power and with limited word length. In particular, WINS applications
are generally tolerant to latency. The WINS node event recognition may be delayed by 10
100 msec, or longer.
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2.3 WINS DIGITAL SIGNAL PROCESSING
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 digital signal processing system. The spectrum analyzer
resolves the WINS input data into a low-resolution power spectrum. Power spectral density
(PSD) in each 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. The WINS spectrum analyzer must operate at W power level. So the complete
WINS system, containing controller and wireless network interface components, achieves
low power operation by maintaining only the micropower components in continuous
operation. The WINS spectrum analyzer system, shown in Figure 7, contains a set of parallelfilters.
Figure 7. WINS micropower spectrum analyzer architecture.
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2.4 WINS MICRO SENSORS
Source signals (seismic, infrared, acoustic and others) all decay in amplitude 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 Sensorstrate 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 thestranger entering the border. These signals are then converted into their PSD values and are
then compared with the reference values set by the user.
Figure 3. Thermal Infrared Detector
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Chapter 3:
ROUTING BETWEEN NODES
The sensed signals are then routed to the major node. This routing is done
based on the shortest distance. That is the distance between the nodes is not considered, but
the traffic between the nodes is considered. This has been depicted in the figure 4. In the
figure, the distances between the nodes and the traffic between the nodes has been clearly
shown. For example, if we want to route the signal from the node 2 to node 4, the shortest
distance route will be from node 2 via node 3 to node 4. But the traffic through this path is
higher than the path node 2 to node 4. Whereas this path is longer in distance.
Figure 4. Nodal distance and Traffic
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3.1 SHORTEST DISTANCE ALGORITHMIn this process we find mean packet delay, if the capacity and average flow are
known. From the mean delays on all the lines, we calculate a flow-weighted average to get
mean packet delay for the whole subnet. The weights on the arcs in the figure 5 give
capacities in each direction measured in kbps.
Figure 5. Subnet with line capacities Figure 6.s Routing Matrix
In fig 6 the routes and the number of packets/sec sent from source to destination are
shown. For example, the E-B traffic gives 2 packets/sec to the EF line and also 2 packets/sec
to the FB line. The mean delay in each line is calculated using the formula
TTii ==11//((cc--))
TTii == TTiimmee ddeellaayy iinn sseecc
C = Capacity of the path in Bps
== MMeeaann ppaacckkeett ssiizzee iinn bbiittss
== MMeeaann ffllooww iinn ppaacckkeettss//sseecc..
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The mean delay time for the entire subnet is derived from weighted sum of all the
lines. There are different flows to get new average delay. But we find the path, which has the
smallest mean delay-using program. Then we calculate the Waiting factor for each path. The
path, which has low waiting factor, is the shortest path. The waiting factor is calculated using
WW ==ii //
ii == MMeeaann ppaacckkeett ffllooww iinn ppaatthh
== MMeeaann ppaacckkeett ffllooww iinn ssuubbnneett
The tabular column listed below gives waiting factor for each path.
Figure 5. WINS Comparator response
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Chapter 4:PSD COMPARISION
Each filter is assigned a coefficient set for PSD computation. Finally,
PSD values are compared with background reference values In the event that the measured
PSD spectrum values exceed that of the background reference values, the operation of a
microcontroller is triggered. Thus, only if an event appears, the micro controller operates.
Buffered data is stored during continuous computation of the PSD spectrum. If an event is
detected, the input data time series, including that acquired prior to the event, are available to
the micro controller. The micro controller sends a HIGH signal, if the difference is high. It
sends a LOW signal, if the difference is low. For a reference value of 25db, the comparison
of the DFT signals is shown in the figure 8.
Figure 8. Comparator plot
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4.1 WINS MICROPOWER EMBEDDED RATIO
WINS systems present novel requirements for low cost, low power, short range, and
low bit rate RF communication. 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. The prototype, operational, WINS networks are implemented with a self-
assembling, multihop TDMA network protocol.The WINS embedded radio development is
directed to CMOS circuit technology to permit low cost fabrication along with the additionalWINS components. In addition, WINS embedded radio design must address the peak current
limitation of typical battery sources, of 1mA. It is critical, therefore, to develop the methods
for design of micropower CMOS active elements. For LC oscillator phase noise power, S, at
frequency offset of away from the carrier at frequency with an input noise power,
Snoise and LC tank quality factor, Q, phase noise power is:
Now, phase noise power, Snoise, at the transistor input, is dominated by 1/f noise.
Input referred thermal noise, in addition, increases with decreasing drain current and power
dissipation due to the resulting decrease in transistor transconductance. The tunability of
micropower CMOS systems has been tested by implementation of several VCO systems to be
discussed below. The embedded radio system requires narrow band operation and must
exploit high Q value components.
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Chapter 5:DESIGN CONSIDERATION
a. Reliability:
The system must be reliable so that the probability of failures and faulty operations
must be very less.
b. Energy: There are four way in which node consumes energy.
Sensing: C h o o s i n g r i g h t s e n s o r f o r t h e j o b c a n i m p r o v e t h e
s y s t e m performance and to consume less power.
Computation: The sen sor must b e chosen so that the speed of computation can be very
fast and less faults.
Storing: The se nsor must have suff icient storage to s tore the sensed da ta so that it
can be communicated.
Communicating: Th e co mm un ic at in g be tw ee n se ns or s is ve ry im po rt an t factor
when it is used for border security. There must not be any faults during communicating the
sensed data between various nodes and the gateway. The sensor must be design to
minimize the likelihood of environment effect of wind, rain, snow etc. The enclosure
is manufacture from clear acrylic material. Otherwise the sensor may damage due to weather
effects and may give fault results.
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Chapter 6:ADVANTAGES, DISADVANTAGES &
APPLICATIONS
6.1 Advantages:
WINS require a microwatt of power so it is very cheaper than other security systemsuch as Radar.
It produce a less amount delay to detect the target. It is reasonably faster.
6.2 Unanticipated faulty behavior:
We may ex p er i en ce sev era l f a i lu res as a r esu l t o f u n d e tec tab le ,
incorrectly download program and depleted energy level etc. For example node
will detect false event when sensor board is overheated. So this unanticipated
faul ty behavior must be overcome by using the suitable protection for the sensors or by
using the proper enclosure as shown in the figure
6.3 Applications:
1. On a global scale, WINS will permit monitoring of land, water, and air resources for
environmental monitoring.
2. On a national scale, transportation systems, and borders will be monitored for efficiency,
safety, and security.
3. On a local, enterprise scale, WINS will create a manufacturing
information service for cost and quality control.
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Chapter 7:Conclusion
A series of interface, signal processing, and communication systems have been
implemented in micropower CMOS circuits. A micropower 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. 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 systems, and borders will be monitored for efficiency, safety,
and security.
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