Published on Apr 02, 2024
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.
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.
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.
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?
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.
There are also example situations (such as radar) in which it is better to concentrate the elements, typically where it is not possible to get sensors close to targets. There are also many situations in which it is possible to place sensors in proximity to targets, bringing many advantages.
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. To build the minimum size space, we must determine the marginal improvement in the decision error rate resulting from addition of another feature. This may be as simple as including another term in an orthonormal expansion (such as fast Fourier transform and wavelet transform) or an entirely different transformation of the set {X}.
Unfortunately, we seldom know the prior probabilities of the various hypotheses; training is often inadequate to determine the conditional probabilities; and the marginal improvement in reliability declines rapidly as more features are extracted from any given set of observables.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.
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