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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. 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.
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 for biomedicine will connect patients in the clinic,ambulatory
outpatient services, and to medical professionals to senseing,monitoring and control.
On a local machine scale,WINS condition based maintenance devices will equip
powerplants, appliances, vehicles, and energy systems for enhancements in reliability,
reductions in energy usage, and improvements in quality of service. The opportunities
for WINS depend on thedevelopment of a 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. Thus, for all of
these applications, local processing of distributed measurement data is required for a
low cost, scalable technology. Distributed signal processing and decision making
enable events to be identified at the remote sensor. Thus, information in the form
ofdecisions 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.
The opportunities for WINS depend on the development of scalable, low cost,
sensor network architecture. This requires these sensor information be conveyed to
the users at 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 sensors. Thus, information in the form of decisions is
conveyed in short message packets. Future applications of distributed embedded
processors and sensors will require massive number of devices. In this paper we have
concentrated in the most important application, border security
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.
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 µ R-a (where a 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 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 Na-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.
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.
Primary LWIM applications require sensor nodes powered by compact battery
cells. Total average system supply currents must be less than 30mA to provide long
operating life from typical compact Li coin cells. 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 a 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.
Distribute network sensor must continuously monitor multiple sensor system,
process sensor signals, and adapt to changing environments and 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. 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 micropower interface circuits must sample at dc or low frequency where “1/f” noise in these CMOS interfaces is
large. The micropower signal processing system must be implemented at low power
and with limited word length. The WINS network supports multihop communication
with a wireless bridge connection to a conventional wireline 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 currentsdegrade the cell energy capacity
through electrode damage.) Both average and peak current requirements present
unique challenges for circuit design. In this paper, the requirements, architectures, and
circuits for micropower WINS systems will be described.