Border Security using WINS
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.
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 microsensor 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.
WINS opportunities depend on development of a scalable,
low-cost, sensor-network architecture. Such applications require delivery of
sensor information to the user at a low bit rate through low-power
transceivers. Future applications of distributed embedded processors and
sensors will require vast numbers of devices. Conventional methods of sensor
networking represent an impractical demand on cable installation and network
bandwidth. Processing at the source would drastically reduce the financial,
computational, and management burden on communication system components,
networks, and human resources.
Here, we limit ourselves to a security application
designed to detect and identify threats within some geographic region and
report the decisions concerning the presence and nature of such threats to a
remote observer via the Internet. In the context of this application, we
describe the physical principles leading to consideration of dense sensor
networks, outline how energy and bandwidth constraints compel a distributed and
layered signal processing architecture, outline why network self-organization
and reconfiguration are essential, discuss how to embed WINS nodes in the
Internet, and describe a prototype platform enabling these functions, including
remote Internet control and analysis of sensor-network operation.
Centralized methods of sensor networking make impractical demands on cable installations and network bandwidth. The burden on communication system components, networks, and human resources can be drastically reduced if raw data are processed at the source and the decisions conveyed.
The
same holds true for systems with relatively thin communications pipes between a
source and the end network or systems with large numbers of devices. The
physical world generates an unlimited quantity of data that can be observed,
monitored, and controlled, but wireless telecommunications infrastructure is
finite.
CHAPTER-2
Physical Principles
When are distributed sensors better than a single large
device, given the high cost of design implicit in having to create a
self-organizing cooperative network? What are the fundamental limits in
sensing, detection theory, communications, and signal processing driving the
design of a network of distributed sensors?
2.1 Propagation laws for sensing.
All signals decay with distance as a wavefront expands.
For example, in free space, electromagnetic waves decay in intensity as the
square of the distance; in other media, they are subject to absorption and
scattering effects that can induce even steeper declines in intensity with
distance. Many media are also dispersive (such as via multipath or low-pass
filtering effects), so a distant sensor requires such costly operations as
deconvolution (channel estimation and inversion) to partially undo the
dispersion. Finally, many obstructions can render electromagnetic sensors
useless. Regardless of the size of the sensor array, objects behind walls or
under dense foliage cannot be detected.
As a simple example, consider the number of pixels needed
to cover a particular area at a specified resolution. The geometry of similar
triangles reveals that the same number of pixels is needed whether the pixels
are concentrated in one large array or distributed among many devices. For free
space with no obstructions, we would typically favor the large array, since
there are no communications costs for moving information from the pixels to the
processor. However, coverage of a large area implies the need to track multiple
targets (a very difficult problem), and almost every security scenario of
interest involves heavily cluttered environments complicated by obstructed
lines of sight. Thus, if the system is to detect objects reliably, it has to be
distributed, whatever the networking cost.
2.2 Detection and estimation theory fundamentals.
A detector is given a set of observables {Xj}
to determine which of several hypotheses {hi} is true. These
observables may, for example, be the sampled output of a seismic sensor. The
signal includes not only the response to the desired target (such as a nearby
pedestrian) but background noise and interference from other seismic sources. A
hypothesis might include the intersection of several distinct events (such as
the presence of multiple targets of particular types).
The decision concerning target presence, absence, and
type is usually based on estimates of parameters of these observations.
Examples of parameters include selected Fourier, linear predictive coding, and
wavelet transform coefficients. The number of parameters is typically a small
fraction of the size of the observable set and thus constitute a reduced
representation of the observations for purposes of distinguishing among
hypotheses.
The set of parameters is known collectively as the
feature set {fk}. The reliability of this parameter estimation
depends on both the number of independent observations and the signal-to-noise
ratio (SNR). For example, according to the Cramer-Rao bound, which establishes
the fundamental limits of estimation accuracy, the variance of a parameter
estimate for a signal perturbed by white noise declines linearly with both the
number of observations and the SNR. Consequently, to have to compute a good
estimate of any particular feature, we need either a long set of independent
observations or high SNR.
The formal means of choosing among hypotheses is to
construct a decision space (whose coordinates are the values of the features)
and divide it into regions according to the rule we decide on the hypothesis hi,
if the conditional probability p(hi|{fk}) > p(hj|{fk})
for all j not equal to i. Note that the features include environmental
variations and other factors we measure or about which we have prior knowledge.
The complexity of the decision increases with the dimension of the feature
space; our uncertainty in the decision also generally increases with the number
of hypotheses we have to sort through. Thus, to reliably distinguish among many
possible hypotheses, we need a larger feature space.
On these facts hang many practical algorithms. For
example, we could apply the deconvolution and target-separation machinery to
exploit a distributed array. Though this machinery requires intensive
communications and computations, it vastly reduces the size of the feature
space and the number of hypotheses that have to be considered, as each feature
extractor deals with only one target with no propagation dispersal effects.
Alternatively, we may deploy a dense sensor network. Due
to the decay of signals with distance, shorter-range phenomena (such as
magnetics) can be used, limiting the number of targets (and hence hypotheses)
in view at any given time. At short range, the probability is enhanced that the
environment is essentially homogeneous within the detection range, reducing the
number of environmental features—and thus the size of the decision space.
Finally, since higher SNR is obtained at short range, and we can use a variety
of sensing modes that may be unavailable at distance, we are better able to
choose a small feature set that distinguishes targets. With only one mode, we
would need to go deep into that mode's feature set, getting lower marginal
returns for each feature. Thus, having targets nearby offers many options for
reducing the size of the decision space.
2.3 Communications constraints.
Spatial separation is another important factor in the
construction of communication networks. For low-lying antennas, intensity drops
as the fourth power of distance due to partial cancellation by a
ground-reflected ray. Propagation is influenced by surface roughness, the
presence of reflecting and obstructing objects, and antenna elevation. The
losses make long-range communication a power-hungry exercise; the combination
of Maxwell's Laws (governing propagation of electromagnetic radiation) and
Shannon's capacity theorem (establishing fundamental relationships among
bandwidth, SNR, and bit rate) together dictate that there is a limit on how
many bits can be conveyed reliably, given power and bandwidth restrictions. On
the other hand, the strong decay of intensity with distance provides spatial
isolation, allowing the reuse of frequencies throughout a network.
Multipath propagation (resulting from reflections off multiple objects) is a serious problem. A digital modulation requires a 40dB increase in SNR to maintain an error probability of 10-5 with Rayleigh distributed-amplitude fading of the signal due to multipath, compared to a channel with the same average path loss perturbed only by Gaussian noise. It is possible to recover most of this loss by means of "diversity" obtainable in any of the domains of space, frequency, and time, since, with sufficient separation, the multipath fade levels are independent. By spreading the information, the multiple versions experience different fading, so the result is more akin to the average. If nothing is done, the worst-case conditions dominate error probabilities.
For static sensor nodes, time diversity is not an option
with respect to path losses, although it may be a factor in jamming and other
types of interference. Likewise, spatial diversity is difficult to obtain,
since multiple antennas are unlikely to be mounted on small platforms. Thus,
diversity is most likely to be achieved in the frequency domain by, say,
employing some combination of frequency-hopped spread spectrum, interleaving,
and channel coding. Measures known to be effective against deliberate jamming
are also generally effective against multipath fading and multiuser
interference. This interference reflects the common problem of intermittent
events of poor SNR.
"Shadowing," or wavefront obstruction and
confinement, and path loss can be dealt with by employing a multihop network.
If nodes are placed randomly in an environment, some links to near neighbors
are obstructed, while others present a clear line of sight. The greater the
density, the closer the nodes and the greater the likelihood of having a link
with sufficiently small distance and shadowing losses. The signals then
effectively hop around obstacles. Exploitation of these forms of diversity can
lead to orders of magnitude reduction in the energy required to transmit data
from one location in a WINS network to another.
CHAPTER-3
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 4.1. The fig.3.1 represents
the general structure of the wireless integrated network sensors (WINS)
arrangement.
CHAPTER-4
WINS NODE ARCHITECTURE
WINS MICRO SENSORS
CHAPTER-6
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 6.1. 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.
CHAPTER-7
SHORTEST DISTANCE ALGORITHM
In 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 7.1 give capacities in each direction measured in kbps.
In fig 7.2 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
Ti = Time delay in sec
C
= Capacity of the path in Bps
µ =
Mean packet size in bits
λ =
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
λi = Mean packet flow in path
λ
= Mean packet flow in subnet
The tabular column listed below gives waiting
factor for each path.
CHAPTER-8
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 mW 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 8.1, contains a set of parallel
filters.
CHAPTER-9
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 9.1.
CHAPTER-10
WINS MICROPOWER EMBEDDED RADIO
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 micropower CMOS active elements. For LC oscillator phase noise power,
Sf, at frequency offset
of dw away from the carrier
at frequency w 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.
CHAPTER-11
CONCLUSION
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.
REFERENCES
- G.I.Pottie, W.J.Kaiser “Wireless
Integrated network sensors”, Communications of the ACM, May 2002.
- C.Shen, C.Srisathapomphat “sensor
networking architecture and application”, IEEC personal communication.
Aug,2001.
- C.Chellappan, RTCBPA, June 2003.
- Pappa,Transducer networks, RTCBPA,
June 2003.
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