ECE-592

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ECE 592

Submitted by

Dugulam Sushma

850939697

BORDER SECURITY USING WINS

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BORDER SECURITY USING

WIRELESS INTEGRATED

NETWORK SENSORS


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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 is 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. 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.

CHAPTER 1


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INTRODUCTION

Wireless Integrated Network Sensors (WINS) provide distributed network and

Internet access to sensors, controls, and processors that are deeply embedded in equipment,

facilities, and the environment. The Wireless Integrated Network Sensors (WINS) network

is a new monitoring and control capability for applications in transportation,

manufacturing, health care, environmental monitoring, and safety and security, border

security. Wireless Integrated Network Sensors WINS networks provide sensing, local

control, and embedded intelligent systems in structures, materials, and environments

Wireless Integrated Network Sensors combine microsensor technology, low power signal

processing, low power computation, and low power, low cost wireless networking

capability in a compact system.

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 wire line 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.

WINS for biomedicine will connect patients in the clinic, ambulatory outpatient services,

and medical professionals to sensing, monitoring, and control. On a local machine scale,

WINS condition based maintenance devices will equip power plants, appliances, vehicles,


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and energy systems for enhancements in reliability, reductions in energy usage, and

improvements in quality of service.

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. Conventional methods for sensor networking would present impractical

demands on cable installation and network bandwidth. By eliminating the requirements for

transmission of all measured data, the burden on communication system components,

networks, and human resources are drastically reduced. In this paper we have concentrated

in the most important application, Border Security.

CHAPTER 2


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WIRELESS INTEGRATED NETWORK SENSORS (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. 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. The WINS architecture, therefore, exploits the small separation between

WINS nodes to provide multihop communication.

Multihop communication (see Figure 2) yields large power and scalability

advantages for WINS networks. First, RF communication path loss has been a primary

limitation for wireless networking, with received power, PREC, decaying as transmission

range, R, as PREC R- (where varies from 3 5 in typical indoor and outdoor

environments). However, in a dense WINS network, multihop architectures may permit N

communication link hops between N+ 1 nodes. In the limit where communication system


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power dissipation (receiver and transceiver power) exceeds that of other systems within the

WINS node, the introduction of N co-linear equal range hops between any node pair

reduces power by a factor of N-1 in comparison to a single hop system. Multihop

communication, therefore, provides an immediate advance in capability for the WINS

narrow bandwidth devices. Clearly, multihop communication raises system complexity.

However, WINS multihop communication networks permit large power reduction and the

implementation of dense node distribution.

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.


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Continuous operation low duty cycle

Figure 1. The wireless integrated network sensor (WINS) architecture.

The wireless integrated network sensor (WINS) architecture includes sensor, data

converter, signal processing, and control functions. Micropower RF communication

provides bidirectional network access for low bit rate, short range communication. The

micropower components operate continuously for event recognition, while the network

interface operates at low duty cycle.


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CHAPTER 3

WINS NODE ARCHITECTURE

WINS Node Architectures development was initiated in 1993 at the University of

California, Los Angeles; the first generation of field-ready WINS devices and software was

fielded there three years later. The DARPA-sponsored low-power wireless integrated

micro sensors (LWIM) project demonstrated the feasibility of multihop, self-assembled,

wireless networks. This first network also demonstrated the feasibility of algorithms for

operating wireless sensor nodes and networks at micropower levels. In another DARPA-

funded joint development program (involving UCLA and the Rockwell Science Center of

Thousand Oaks, Calif.), a modular development platform was devised to enable evaluation

of more sophisticated networking and signal-processing algorithms and to deal with many

types of sensors, though with less emphasis on power conservation than LWIM. These

experiments taught us to recognize the importance of separating the real-time functions

that have to be optimized for low power from the higher-level functions requiring

extensive software development but that are invoked with light-duty cycles.

The WINS NG node architecture was subsequently developed by Sensor.com,

founded by the authors in 1998 in Los Angeles, to enable continuous sensing, signal

processing for event detection, local control of actuators, event identification, and

communication at low power. Since the event-detection process is continuous, the sensor,

data converter, data buffer, and signal processing all have to operate at micropower levels,

using a real-time system. If an event is detected, a process may be alerted to identify the

event. Protocols for node operation then determine whether extra energy should be


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expended for further processing and whether a remote user or neighboring WINS node

should be alerted. The actuator continuously vigilant operation low-duty cycle operation

WINS node then communicates an attribute of the identified event, possibly the address of

the event in an event look-up table stored in all network nodes. These infrequent events can

be managed by the higher-level processors in the first version of WINS NG, a Windows

CE-based device selected for the availability of low-cost developer tools. By providing

application programming interfaces enabling the viewing and controlling of the lower-level

functions, a developer is either shielded from real-time functions or is allowed to delve into

them as desired to improve applications efficiency.

Future generations will also support plug-in Linux devices; other development will

include very small but limited sensing devices that interact with WINS NG nodes in

heterogeneous networks. These small devices might scavenge their energy from the

environment by means of photocells or piezoelectric materials, capturing energy from

vibrations and achieving perpetual life spans. A clear technical path exists today, offering

increased circuit integration and improved packaging. This path should produce very low-

cost and compact devices in the near future.

Low power, reliable and efficient network operation is obtained with intelligent

sensor nodes that include sensor signal processing, control, and a wireless network

interface. The signal processor described here can supply a hierarchy of information to the

user ranging from single-bit event detection, to power spectral density (PSD) values, to

buffered, real time data. This programmable system matches its response to the power and

information requirements. Distributed network sensor devices must continuously monitor

multiple sensor systems, process sensor signals, and adapt to changing environments and


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user requirements, while completing decisions on measured signals. Clearly, for low power

operation, network protocols must minimize the operation duty cycle of the high power RF

communication system.

Unique requirements for the WINS node appear for sensors and micropower

sensor interfaces. The WINS network supports multihop communication (see Figure 2)

with a wireless bridge connection to a conventional wire line network service.

While unique requirements exist for low power node operation, there is a balancing

set of unique operational characteristics that permit low power operation if properly

exploited. In particular, WINS applications are generally tolerant to latency. Specifically,

in contrast to conventional wireless network applications where latency is not tolerated, the

WINS node event recognition may be delayed by 10 100 msec, or longer. This permits

low clock rate signal processing and architecture design that minimizes computation and

communication power at the expense of latency. For example, in the latency-tolerant

WINS system, time division multiple access protocols may be implemented to reduce

communication power. Also, it is important to note that sensor signals are generally

narrowband signals (bandwidth less than 10kHz) that require only low sample and

processing rates.

Many of the primary WINS applications require sensor nodes powered by compact

battery cells. Total average system supply currents must be less than 30A to provide long

operating life from typical compact Li coin cells (2.5 cm diameter and 1 cm thickness). In

addition, these compact cells may provide a peak current of no greater than about 1 mA

(higher peak currents degrade the cell energy capacity through electrode damage.) Both

average and peak current requirements present unique challenges for circuit design. In this


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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 micro power signal processing

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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CHAPTER 4

WINS MICRO SENSORS

Many important WINS applications require the detection of signal sources in the

presence of environmental noise. 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. WINS sensor integration

relies on structures that are flip-chip bonded to a low temperature, co-fired ceramic

substrate. The sensor-substrate Sensors rate 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 converted into their PSD values and are then

compared with the reference values set by the user.


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Figure 3. Thermal Infrared Detector

4.1 WINS MICRO SENSOR INTERFACE CIRCUITS

The WINS micro sensor systems must be monitored continuously by the CMOS

micro power 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

micro sensor applications is less than 1kHz (for example the infrared micro sensor

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 thermopile 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 (-) converter is chosen over

other architectures due to power constraints. The - architecture is also compatible with

the limitations of low cost digital CMOS technologies

4.2 REMOTE BATTLE FIELD SENSOR SYSTEM (REMBASS):


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REMBASS is a ground-based, all-weather, day-and-night, battlefield surveillance,

target development, and early warning system capable of remote operation under field

conditions. The basic purpose of REMBASS is to detect, locate, classify, and report

personnel and vehicular (wheeled and tracked) activities in real-time within the area of

deployment. With a meteorological sensor attached, it will also sense and collect weather

information. It uses remotely monitored sensors emplaced along likely enemy avenues of

approach. These sensors respond to seismic-acoustic energy, infrared energy, and magnetic

field changes to detect enemy activities. The sensors process the data and provide detection

of classification information which is incorporated into digital messages and transmitted

through short burst transmission to the system sensor monitor programmer set. The

messages are demodulated, decoded, displayed, and recorded to provide a time-phased

record of enemy activity.

Figure 4. REMBASS

This system complements other manned/unmanned surveillance systems such as

ground surveillance radar, unmanned aerial vehicles, and night observation devices. The


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system provides division, brigade, and battalion commanders with information from

beyond the forward line of own troops (FLOT), and enhances rear area protection. It can be

deployed anywhere in the world in a tactical environment in support of reconnaissance,

surveillance, and target acquisition (RSTA) operations. The system consists of eleven

major components: a passive infrared (IR) sensor, magnetic (MAG) sensor,

seismic/acoustic (SA) sensor, radio repeater. Sensor Monitoring Set (SMS), radio

frequency monitor (referred to as portable monitoring set (PMS)), code programmer,

antenna group, power supply, mounting rack, and Sensor Signal Simulator (SSS). A set

consists of eight IR sensors, eight MAG sensors, thirty-two SA sensors, eight radio

repeaters, one SMS, three PMS, two code programmers, one antenna group, one power

supply, one mounting rack, and one SSS.

(1) Magnetic Sensor: This is a hand-emplaced, MAG sensor. The MAG sensor detects

vehicles (tracked or wheeled) and personnel carrying ferrous metal. It also provides

information on which to base a count of objects passing through its detection zone and

reports their direction of travel relative to its location. The monitor uses two different

(MAG and IR) sensors and their identification codes to determine direction of travel.

(2) Seismic Acoustic Sensor: This is a hand-emplaced SA classifying sensor. It detects

targets and classifies them as unknown, wheeled vehicle, tracked vehicle, or personnel.

(3) Passive Infrared Sensor: This is a hand-emplaced, IR detecting sensor. The sensor

detects tracked or wheeled vehicles and personnel. It also provides information on which to

base a count of objects passing through its detection zone and reports their direction of

travel relative to its location. The monitor uses two different (MAG and IR) sensors and

their identification codes to determine direction of travel.


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(4) Radio Repeater: This is an expendable/recoverable, digital/analog radio repeater used

to extend the broadcast range of radio messages from anti-intrusion sensors to a monitoring

set. It receives processes and relays messages from either an anti-intrusion sensor or

another like radio repeater. Several repeaters may be used in a station-to-station chain, one

sending to another, to relay messages over a long distance.

(5) Sensor Monitoring Set: The SMS has a dual channel receiver with a permanent hard

copy recorder and a temporary visual display (TVD). The SMS receives processes,

displays, and records sensor information relating to 60 sensor ID codes. Detections and

classification are displayed as: dashes (-) for unknown targets, (T) for tracked vehicles,

(W) for wheeled vehicles, and (P) for personnel. The TVD can simultaneously display up

to ten sensor ID codes with detection or classification information. A keyboard allows the

operator to program the SMS operation: set radio frequency (RF) channels, establish hard

copy recorder format, initiate system operational checks or built in test (BIT), and calculate

target speed. A separate display shows the keyboard functions and calculations.

(6) Radio Frequency Monitor: This is a single-channel PMS with a TVD. The PMS

receives, processes, and displays sensor ID codes and detection/classification messages.

(7) Code Programmer: The programmer is a portable device used to program sensors and

repeaters to the desired operating channel, ID code, mission life, arm mode, and gain. It is

also used to condition newly installed batteries in sensors and repeaters. It has a built in

visual self test to ensure the proper information programmed into the sensor or repeater.

(8) Antenna Group: The antenna group consists of an omni directional unity gain

antenna, a mast assembly, a pre-amplifier suitable for mast mounting and an RF


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multicoupler. It is used with the SMS and the PMS. Up to four monitoring devices can use

the antenna group simultaneously.

(9) Power Supply: The power supply is a custom fly back-type switching regulator that

converts external power sources (24 volts direct current (dc), 115 or 220 volts alternating

current) to 12 volts dc nominal prime power. The power supply can be used to power the

SMS, repeater or SSS.

(10) Mounting Rack: The mounting rack is an aluminum angle shock mounted rack. It is

used to mount the repeaters in helicopters.

(11) Sensor Signal Simulator (SSS): The SSS is similar in appearance to the SMS. It has

the capability to receive, record, edit, copy, and retransmit an operational scenario

involving any two of the 599 REMBASS channels. It also has the capability to transmit

pre-recorded scenarios. These functions are accomplished without any additional support

equipment. The SSS allows institutional or unit sustainment training in either a classroom

or field environment without the use of REMBASS/IREMBASS sensors. The operator can

monitor the outputs of the SSS on the PMS or SMS.

CHAPTER 5

ROUTING BETWEEN NODES


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Figure 6. Subnet with line capacities Figure 7.s Routing Matrix

In fig 7 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 =1/(c-=1/(c-))

Ti = Time delay in sec

C = Capacity of the path in Bps

= Mean packet size in bits

= Mean flow in packets/sec.

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


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W =W = i /i /

i = Mean packet flow in pathi = Mean packet flow in path

= Mean packet flow in subnet= Mean packet flow in subnet

The tabular column listed below gives waiting factor for each path.

Figure 5. WINS Comparator response

CHAPTER 6

WINS DIGITAL SIGNAL PROCESSING


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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. 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 8, contains a set of

parallel filters. Mean square power for each frequency bin, is computed at the output of

each filter. Of course, the microcontroller may support additional, more complex

algorithms that provide capability (at higher power) for event identification.

The WINS spectrum analyzer architecture includes a data buffer, shown in Figure

8. 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 microcontroller. Low power operation of the spectrum analyzer is achieved

through selection of an architecture that provides the required performance and function

while requiring only limited word length. First, since high resolution measurements of PSD

are required (5 Hz bandwidth passbands at frequencies of 5 50 Hz with a 200 Hz input


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word rate) FIR filters would require an excessive number of taps and corresponding power

dissipation. In contrast, IIR filter architectures have provided adequate resolution with

limited word length.

Figure 8. WINS micro power spectrum analyzer architecture.

6.1 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 9.


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Figure 9. Comparator plot

CHAPTER 7

WINS MICROPOWER EMBEDDED RADIO


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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 additional WINS 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 micro power

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


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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.

CHAPTER 8

CHARACTERISTICS AND APPLICATIONS


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CHARACTERISTICS

The following are the characteristics of the wireless integrated network sensors

(WINS)

It supports large numbers of sensor.

Dense sensor distributions.

Low-power signal processing, computation, and low-cost wireless networking.

These sensors are also developed to support short distance RF communication.

Internet access to sensors, controls and processor is easy.

APPLICATIONS

Wireless integrated network sensors (WINS) provide distributed network and Internet

access to sensors, controls, and processors deeply embedded in equipment, facilities, and

the environment.

The WINS network represents a new monitoring and control capability for applications

in such industries as transportation, manufacturing, health care, environmental

oversight, and safety and security.

WINS combine micro sensor technology and low-power signal processing,

computation, and low-cost wireless networking in a compact system.

Recent advances in integrated circuit technology have enabled construction of far more

capable yet inexpensive sensors, radios, and processors, allowing mass production of

sophisticated systems linking the physical world to digital data networks.


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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


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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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ECE-592

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