seminar report

Wireless integrated network sensors 2009-10

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 mbedded 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, 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 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 the development 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 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.

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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 [12]. 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.

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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.

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).

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 [7, 9]. Propagation is

influenced by surface oughness, 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.

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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.

2.4 Energy consumption in integrated circuits: - Unfortunately, there are limits to the

energy efficiency of complimentary metal oxide semiconductor (CMOS) communications and

signal-processing circuits. Overall system costs cannot be low if the energy system is large. A

CMOS transistor pair draws power each time it is flipped. The power used is roughly

proportional to the product of the switching frequency, the area of the transistor (related to

device capacitance), and the square of the voltage swing.

Processing also gets cheaper with time but is not yet free. Because application-specific

integrated circuits (ASICs) can clock at much lower speeds and use less numerical precision,

they consume several orders of magnitude less energy than digital signal processors (DSPs).

While the line between dedicated processors and general-purpose (more easily programmed)

machines is constantly shifting, generally speaking, a mixed architecture is needed for

computational systems dealing with connections to the physical world. The ratio in die area

between the two approaches— ASIC and DSP—scales with technological change, so ASICs

maintain a cost advantage over many chip generations. Convenient programmability across

several orders of magnitude of energy consumption and data processing requirements is a worthy

research goal for pervasive computing. In the meantime, while researchers continue to pursue

that goal, multiprocessor systems are needed in WINS.

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Wins has four Nodes

1. System Archictecture

2. Node Archictecture

3. Network Archictecture

4. Micro sensors

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 2. The figure 1 represents the general structure of the wireless integrated network sensors

(WINS) arrangement.

4. WINS Node Architectures

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WINS development was initiated in 1993 at the University of California, Los Angeles;

the first eneration of field-ready WINS devices and software was fielded there three years later

(see Figure 2a). The DARPA-sponsored low-power wireless integrated microsensors (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 icropower 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 [1]. 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 evelopment 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 (see

Figure 3). 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 expended for further processing and whether a

remote user or neighboring WINS node should be alerted. The 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 processor—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 an application’s efficiency. Future

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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,

supporting, say, intelligent tags (see Borriello’s and Want’s “Embedded Computation Meets the

World Wide Web” in this issue). 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.

5. WINS Network Architecture

Unlike conventional wireless networks, a WINS network has to support large numbers of

sensors in a local area with short range and low average bit-rate communication (fewer than than

1–100Kbps). The network design has to address the requirement of servicing dense sensor

distributions, emphasizing recovery of environmental information. In WINS networks, as a rule,

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we seek to exploit the short-distance separation between nodes to provide multihop

communication through the power advantages outlined earlier. Since for short hops, transceiver

power consumption for reception is nearly the same as that of transmission, the protocol should

be designed so radios are off as much of the time as possible. That is, a device’s medium access

control (MAC) address in a network should include some variant of time division multiple

access.

A time-division protocol requires that the radios exchange short messages periodically to

maintain local synchronism. It is not necessary for all nodes to have the same global clock, but

the local variations from link to link should be small to minimize the guard times between slots

and enable cooperative signal processing functions, including fusion and beamforming. The

messages can combine network performance information, maintenance of synchronization, and

reservation requests for bandwidth for longer packets. The abundant bandwidth resulting from

the spatial reuse of frequencies and local processing ensures relatively few conflicts in these

requests, so simple mechanisms can be used. At least one low-power protocol suite embodying

these principles has been developed, including boot-up, MAC, energy-aware routing, and

interaction with mobile units [8]. Its development indicates the feasibility of achieving

distributed low-power operation in a flat multihop network.

Also clear is that for a wide range of applications, some way has to be found to

conveniently link sensor networks to the Internet. Inevitably, some layering of the protocols (and

devices) is needed to make use of these standard interfaces. For example, the WINS NG (next-

generation) node architecture design (discussed later) addresses the constraints on robust

operation, dense and deep distribution, interoperability with conventional networks and

databases, operating power, scalability, and cost (see Figure 2). WINS gateways provide support

for the WINS network and access between conventional network physical layers and their

protocols and between the WINS physical layer and its low-power protocols. WINS system

design exploits the reduced link range available through multihopping to provide advantages the

system architect can choose from the following set: reduced operating power, improved bit rate,

improved bit error rate, improved communication privacy (by way of reduced transmit power),

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simplified protocols, and reduced cost. These benefits are not obtained simultaneously but need

to be extracted individually, depending on design emphasis.

In network design today, architects also have to address: How can Internet protocols,

including TCP and IPv6, be employed within sensor networks? While it is desirable to not have

to develop new protocols or perform protocol conversion at gateways, several factors demand

custom solutions. First, IPv6 is not truly self-assembling; while addresses can be obtained from a

server, this particular protocol presupposes attachment at lower levels already. Second, present-

day Internet protocols take little account of the unreliability of physical channels or the need to

conserve energy, focusing instead on support for a wide range of traffic. Embedded systems can

achieve far higher efficiencies by exploiting the traffic’s limited nature.

Another question they have to address is: Where should the processing and storage take

place? Communication costs a great deal compared to processing; therefore energy constraints

dictate doing as much processing at the source as possible. Moreover, reducing the amount of

data to transmit simplifies network design significantly, permitting scalability to thousands of

nodes per Internet gateway.

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6. WINS Microsensors

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. This sensor-substrate

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“sensorstrate” is then a platform for support of interface, signal processing, and communication

circuits. Examples of WINS microseismometer and infrared detector devices are shown in Figure

3.[1].

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 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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7. WINS Microsensor Interface Circuits

The WINS microsensor systems must be monitored continuously by the CMOS

micropower analog-to-digital converter (ADC). As was noted above, power requirements

constrain the ADC design to power levels of 30uW or less. Sensor sample rate for typical

microsensor applications is less than 1kHz (for example the infrared microsensor 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 themopile infrared sensor may beemployed 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

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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.

The analog components of the ADC operate in deep sub threshold to meet the goal of

micro power operation [2]. This imposes severe bandwidth restrictions on the performance of the

circuits within the loop. A high over sampling ratio of 1024 is thus chosen to overcome the

problems associated with low performance circuits. The possible increased power consumption

of digital components in the signal path including the low pass filter is minimized with the use of

low power cell libraries and architecture.

Implementation of low noise ADC systems in CMOS encounters severe “1/f” input noise

with input noise corner frequencies exceeding 100 kHz. The WINS ADC applications are

addressed by a first-order converter architecture combined with input signal switching (or

chopping). The chopper ADC heterodynes the input signal to an intermediate frequency (IF)

before delivery to the S-D loop.

An IF frequency of 1/8th of the ADC sampling frequency is chosen. The low thermopile

sensor source impedance limits the amplitude of charge injection noise that would result from

signal switching. The required demodulation of the IF signal to the desired baseband is

accomplished on the digital code modulated signal, rather than on the analog signals. This both

simplifies architecture and avoids additional injected switching noise. The architecture of the

chopped S-D ADC is shown in Figure 4.

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The first order ADC has been fabricated in the HPCMOS 0.8process (Figure 5). Direct

measurement shows that the converter achieve greater than 9 bit resolution for a 100 Hz band

limited signal with a power consumption of only 30W on a single 3V rail. This chopper ADC has

been demonstrated to have a frequency-independent SNR from 0.1 – 100Hz (Figure 6). This

resolution is adequate for the infrared sensor motion detection and temperature measurement

applications

8. WINS Digital Signal Processing

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. Typical events for many

applications generate harmonic signals that may 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 8-bit ADC input data into a low

resolution power spectrum. Power spectral density (PSD) in each of 8

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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. Since this system

must operate continuously, as for the ADC, discussed above, the WINS spectrum analyzer must

operate at uw power level.

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

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

filter. Each filter is assigned a coefficient set for PSD computation. Finally, PSD values are

compared with background reference values (that may be either downloaded or learned). 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 does the

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microcontroller operate. Of course, the microcontroller may support additional, more complex

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

The WINS spectrum analyzer [3] architecture includes a data buffer, shown in Figure 7.

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

pass bands at frequencies of 5 – 50 Hz with a 200 Hz input 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. An example of the

performance of a typical filter is shown in Figure 8. Here, a series of input signals at frequencies

of 10 – 70 Hz were applied to the 8-bit data IIR filter with coefficients selected for a pass band of

10 Hz width centered at 45 Hz. This device dissipates 3uw at 3V bias and at a 200Hz word rate.

9. 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.

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10. 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 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

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Ti =1/(µc-Ti =1/(µc-λλ))

Ti = Time delay in secTi = Time delay in sec

C = Capacity of the path in Bps

µ = Mean packet size in bits µ = Mean packet size in bits

λλ = Mean flow in packets/sec. = 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

W = W = λλi / i / λλ

λλi = Mean packet flow in pathi = Mean packet flow in path

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

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

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Figure 5. WINS Comparator response

11. 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.

12. 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

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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, 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.

13. Wireless Sensor Networks

Sensor networks are the key to gathering the information needed by smart environments,

whether in buildings, utilities, industrial, home, shipboard, transportation systems automation, or

elsewhere. Recent terrorist and guerilla warfare countermeasures require distributed networks of

sensors that can be deployed using, e.g. aircraft, and have self-organizing capabilities. In such

applications, running wires or cabling is usually impractical. A sensor network is required that is

fast and easy to install and maintain.

IEEE 1451 and Smart Sensors G.C.O.E Jalgaon 20


Wireless sensor networks satisfy these requirements. Desirable functions for sensor nodes

include: ease of installation, self-identification, self-diagnosis, reliability, time awareness for

coordination with other nodes, some software functions and DSP, and standard control protocols

and network interfaces [IEEE 1451 Expo, 2001].

There are many sensor manufacturers and many networks on the market today. It is too

costly for manufacturers to make special transducers for every network on the market. Different

components made by different manufacturers should be compatible. Therefore, in 1993 the IEEE

and the National Institute of Standards and Technology (NIST) began work on a standard for

Smart Sensor Networks. IEEE 1451, the Standard for Smart Sensor Networks was the result. The

objective of this standard is to make it easier for different manufacturers to develop smart

sensors and to interface those devices to networks.

Smart Sensor, Virtual Sensor. The figure shows the basic architecture of IEEE 1451

[Conway and Hefferman 2003]. Major components include STIM, TEDS, TII, and NCAP as

detailed in the figure. A major outcome of IEEE 1451 studies is the formalized concept of a

Smart Sensor. A smart sensor is a sensor that provides extra functions beyond those necessary

for generating a correct representation of the sensed quantity [Frank 2000]. Included might be

signal conditioning, signal processing, and decision-making/alarm functions. A general model of

a smart sensor is shown in the figure. Objectives for smart sensors include moving the

intelligence closer to the point of measurement; making it cost effective to integrate and maintain

distributed sensor systems; creating a

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confluence of transducers, control, computation, and communications towards a common goal;

and seamlessly interfacing numerous sensors of different types. The concept of a Virtual Sensor

is also depicted. A virtual sensor is the physical sensor/transducer, plus the associated signal

conditioning and digital signal processing (DSP) required to obtain reliable estimates of the

required sensory information. The virtual sensor is a component of the smart sensor.

Transducers and Physical Transduction Principles

A transducer is a device that converts energy from one domain to another. In our

application, it converts the quantity to be sensed into a useful signal that can be directly

measured and processed. Since much signal conditioning (SC) and digital signal processing

(DSP) is carried out by electronic circuits, the outputs of transducers that are useful for sensor

networks are generally voltages or currents. Sensory transduction may be carried out using

physical principles, some of which we review here. Microelectromechanical Systems (MEMS)

sensors are by now very well developed and are available for most sensing applications in

wireless networks. References for this section include Frank [2000], Kovacs [1998], Madou

[1997], de Silva [1999].

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Mechanical Sensors include those that rely on direct physical contact. The Piezo resistive

Effect converts an applied strain to a change in resistance that can be sensed using electronic

circuits such as the Wheatstone Bridge (discussed later). Discovered by Lord Kelvin in 1856,

the relationship is , with R the resistance, _ the strain, and S the gauge factor which depends on

quantities such as the resistivity and the Poisson’ ratio of the material. There may be a quadratic

term in _ for some materials. Metals and semiconductors exhibit piezoresistivity. The

piezoresistive effect in silicon is enhanced by doping with boron (p-type silicon can have a

gauge factor up to 200). With semiconductor strain gauges, temperature compensation is

important.

The Piezoelectric Effect, discovered by the Curies in 1880, converts an applied stress

(force) to a charge separation or potential difference. Piezoelectric materials include barium

titanate, PZT, and single-crystal quartz. The relation between the change in force F and the

change in voltage V is given by _, where k is proportional to the material charge sensitivity

coefficients and the crystal thickness, and inversely proportional to the crystal area and the

material relative permittivity. The piezoelectric effect is reversible, so that a change in voltage

also generates a force and a corresponding change in thickness. Thus the same device can be

both a sensor and an actuator. Combined sensor/actuators are an intriguing topic of current

research.

Tunneling Sensing depends on the exponential relationship between the tunneling current I

and the tip/surface separation z given by , where k depends on the tunnel barrier height in ev.

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Tunneling is an extremely accurate method of sensing nanometer-scale displacements, but its

highly nonlinear nature requires the use of feedback control to make it useful. K

Capacitive Sensors typically have one fixed plate and one movable plate. When a force is

applied to the movable plate, the change in capacitance C is given as , with the resulting

displacement, A the area, and _ the dielectric constant. Changes in capacitance can be detected

using a variety of electric circuits and converted to a voltage or current change for further

processing. Inductive sensors, which convert displacement to a change in inductance, are also

often useful.

Resonant Temperature Sensors rely on the fact that single-crystal SiO2 exhibits a change

in resonant frequency depending on temperature change. Since this is a frequency effect, it is

more accurate than amplitude-change effects and has extreme sensitivity and accuracy for small

temperature changes.

Optical Transducers convert light to various quantities that can be detected [Kovacs 1998].

These are based on one of several mechanisms. In the photoelectric effect (Einstein, Nobel Prize,

1921) one electron is emitted at the negative end of a pair of charged plates for each light photon

of sufficient energy. This causes a current to flow. In photoconductive sensors, photons generate

carriers that lower the resistance of the material. In junction-based photosensors, photons

generate electron-hole pairs in a semiconductor junction that causes current flow. This is often

misnamed the photovoltaic effect. These devices include photodiodes and phototransistors.

Thermopiles use a thermocouple with one junction coated in a gold or bismuth black absorber,

which generates heat on illumination

Solar cells are large photodiodes that generate voltage from light. Bolometers consist of two

thermally sensitive resistors in a Wheatstone bridge configuration, with one of them shielded

from the incident light. Optical transducers can be optimized for different frequencies of light,

resulting in infrared detectors, ultraviolet detectors, etc. Various devices, including

accelerometers, are based on optical fiber technology, often using time-of-flight information.

G.C.O.E Jalgaon 24


Thermocouples are based on the thermoelectric Seebeck effect, whereby if a circuit consists

of two different materials joined together at each end, with one junction hotter than the other, a

current flows in the circuit. This generates a Seebeck voltage given approximately by V with T2

the temperatures at the two junctions. The coefficients depend on the properties of the two

materials. Semiconductor thermocouples generally have higher sensitivities than do metal

thermocouples. Thermocouples are inexpensive and reliable, and so are much used. Typical

thermocouples have outputs on the order of 50 _V/oC and some are effective for temperature

ranges of -270oC to 2700oC.

Resonant Temperature Sensors rely on the fact that single-crystal SiO2 exhibits a change

in resonant frequency depending on temperature change. Since this is a frequency effect, it is

more accurate than amplitude-change effects and has extreme sensitivity and accuracy for small

temperature changes.

14. Applications

1. Medical Applications of the Future Advances in wireless sensor networking have opened up new opportunities in

healthcare systems. The future will see the integration of the abundance of existing specialized

medical technology with pervasive, wireless networks. They will co-exist with the installed

infrastructure, augmenting data collection and real-time response. Examples of areas in which

future medical systems can benefit the most from wireless sensor networks are in-home

assistance, smart nursing homes, and clinical trial and research augmentation.

As the world's population ages [3], those suffering from diseases of the elderly will

increase. In-home pervasive networks may assist residents by providing memory enhancement,

control of home appliances, medical data lookup, and emergency communication.

Unobtrusive, wearable sensors will allow vast amounts of data to be collected and

mined for next-generation clinical trials. Data will be collected and reported automatically,

G.C.O.E Jalgaon 25


reducing the cost and inconvenience of regular visits to the physician. Therefore, many more

study participants may be enrolled, benefiting biological, pharmaceutical, and medical-

applications research.

2. Critical Development Areas

2.1 Enabling Technologies for Future Medical Devices:

- Interoperability: As a result of the heterogeneity present in the system, communication

between devices may occupy multiple bands and use different protocols. For example, motes

use unlicensed bands for general telemetry or ISM equipment. Implanted medical devices may

use a licensed band allocated for that purpose by the FCC. In order to avoid interference in the

increasingly crowded unlicensed ISM band, biomedical devices may use the WMTS band

(wireless medical telemetry services, at 608 MHz). [1] The homecare network must provide

middleware interoperability between disparate devices, and support unique relationships

among devices, such as implants and their outside controllers.

- Real-time data acquisition and analysis: The rate of collection of data is higher in this type of

network than in many environmental studies. Efficient communication and processing will be

essential. Event ordering, time-stamping, synchronization, and quick response in emergency

situations will all be required.

- Reliability and robustness: Sensors and other devices must operate with enough reliability to

yield high-confidence data suitable for medical diagnosis and treatment. Since the network will

not be maintained in a controlled environment, devices must be robust.

- New node architectures: The integration of different types of sensors, RFID tags, and back-

channel long-haul networks may necessitate new and modular node architectures.

G.C.O.E Jalgaon 26


2.2 Embedded, Real-Time, Networked System Infrastructures for MDSS:

- Patient and object tracking: Tracking can be considered at three levels: symbolic (e.g., Room

136 or X-Ray Lab); geographical (GPS coordinates of a patient on an assisted living campus);

relational/associational ("Dr. Marvin is currently with Patient Bob"). It is complicated by the

presence of multiple patients, non-patient family members, and leaving the range of the home

network.

- Communication amid obstructions and interference: In-building operation has more multi-

path interference due to walls and other obstructions, breaking down the correlation between

distance and connectivity even further. Unwanted emissions and glitching are likely to be

rigorously restricted and even monitored due to safety concerns, particularly around traditional

life-critical medical equipment.

- Multi-modal collaboration and energy conservation: Limited computational and radio

communication capabilities require collaborative algorithms with energy-aware communication.

Richly varied data will need to be correlated, mined, and altered. Heterogeneous devices will be

on very different duty-cycles, from always-on wired-power units to tiny, stealthy, wearable units,

making rendezvous for communication more difficult.

- Multi-tiered data management: Data may be aggregated and mined at multiple levels, from

simple on-body filtering to cross-correlation and history compression in network storage nodes.

Embedded real-time databases store data of interest and allow providers to query them.

2.3 Medical Practice-Driven Models and Requirements:

- Records and data privacy and security: Data collected by the network is sensitive, and

ownership issues are not always clear. It is likely that the healthcare provider owns the sensor

and network devices, yet the data pertain to the patient. Data must be available during

emergencies, but access should leave a non-repudiable “trail," so abuses can be detected. Any

G.C.O.E Jalgaon 27


priority-override mechanisms must be carefully designed. One may want to filter out “privacy-

contaminated” data, for example, a patient walks into the wrong room. The system should not

“leak” this information through sensors being monitored in the room.

- Role-based access control and delegation in real-time: Doctors may delegate access privileges

to other doctors and nurses; family members may monitor quality-of-care for nursing home

residents. The system may have DRM-like issues: “read but not copy,” “view but not save," etc.

Also, patients may have read but not write privileges for the collected sensor data, in order to

avoid fraud.

- Unobtrusive operation: Stealthiness is desirable, particularly for in-home and nursing home

applications, where intrusive technology may not be tolerated. “Invisible” sensors are both

socially more acceptable (draw less attention, more dignified) and more dangerous (unwanted

tagging and surveillance).

3. Roadmap: Next Generation Smart Homecare

We propose a wireless sensor network architecture for smart homecare that possesses the

essential elements of each of the future medical applications, namely:

- Integration with existing medical practices and technology,

- Real-time, long-term, remote monitoring,

- Miniature, wearable sensors, and

- Assistance to the elderly and chronic patients.

It extends healthcare from the traditional clinic or hospital setting to the patient's home,

enabling telecare without the prohibitive costs of retrofitting existing dwellings. Currently,

patients visit doctors at regular intervals, self-reporting experienced symptoms, problems, and

conditions. Doctors conduct various tests to arrive at a diagnosis and then must monitor patient

progress throughout treatment. In smart homecare, the WSN collects data according to a

physician's specifications, removing some of the cognitive burden from the patient (who may

G.C.O.E Jalgaon 28


suffer age-related memory decline) and providing a continuous record to assist diagnosis. In-

home tasks are also made easier, for example, remote device control, medicine reminders, object

location, and emergency communication. The architecture is multi-tiered, with both lightweight

mobile components and more powerful stationary devices. Sensors are heterogeneous, and all

integrate into the home network. Multiple patients and their resident family members are

differentiated for sensing tasks and access privileges

.

Smart homecare benefits both the healthcare providers and their patients. For the

providers, an automatic monitoring system is valuable for many reasons. Firstly, it frees human

labor from 24/7 physical monitoring, reducing labor cost and increasing efficiency. Secondly,

wearable sensor devices can sense even small changes in vital signals that humans might

overlook, for example, heart rate and blood oxygen levels. Quickly notifying doctors of these

changes may save human lives. Thirdly, the data collected from the wireless sensor network can

be stored and integrated into a comprehensive health record of each patient, which helps

physicians make more informed diagnoses. Eventually, the analyzing, diagnosis, treatment

process may also be semi-automated, so a human physician can be assisted by an “electronic

physician."

Healthcare patients benefit from improved health as a result of faster diagnosis and

treatment of diseases. Other quality-of-life issues, such as privacy, dignity, and convenience, are

supported and enhanced by the ability to provide services in the patient's own home. Family

members and the smart homecare network itself become part of the healthcare team. Finally,

memory aids and other patient-assistance services can restore some lost independence, while

preserving safety.

G.C.O.E Jalgaon 29


15. Conclusion

These physical considerations are making it possible for us to pursue the innovative

design of densely distributed sensor networks and the resulting advantages of layered and

heterogeneous processing and networking architectures for related applications. The close

intertwining of network processing is a central feature of systems connecting the physical and

virtual worlds. Development platforms are now available that will increasingly allow a broader

community to engage in fundamental research in networking and new applications, advancing

developers and users alike toward truly pervasive computing. It produces a less amount of delay.

Hence it is reasonably faster. A micropower spectrum analyzer has been developed to enable low

power operation of the entire WINS system.

G.C.O.E Jalgaon 30


16. References: -

1.Agre, J., Clare, L., Pottie, G., and Romanov, N. Development platform for self-organizing wireless sensor networks. In Proceedings of Aerosense’99 (Orlando Fla., Apr. Apr. 8–9). International Society of Optical Engineering, Bellingham, Wa., 1999, 257–268.

2. Asada, G., Dong, M., Lin, T., Newberg, F., Pottie, G., Marcy, H., and Kaiser, W. Wireless integrated network sensors: Low-power systems on a chip. In Proceedings of the 24th IEEE European Solid-State Circuits Conference (Den Hague, The Netherlands, Sept. 21–25). Elsevier, 1998, 9–12.

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3. Bult, K., Burstein, A., Chang, D., Dong, M., Fielding, M., Kruglick, E., Ho, J., Lin, F., Lin, T.-H., Kaiser, W., Marcy, H., Mukai, R., Nelson, P., Newberg, F., Pister, K., Pottie, G., Sanchez, H., Stafsudd, O., Tan, K., Ward, C., Xue, S., and Yao, J. Low-power systems for wireless microsensors. In Proceedings of the International Symposium on Low- Power Electronics and Design (Monterey, Calif., Aug. 12–14). IEEE, New York, 1996, 17–21.

4. Dong, M., Yung, G., and Kaiser, W. Low-power signal processing architectures for network microsensors. In Proceedings of the 1997 International Symposium on Low-Power Electronics and Design (Monterey, Calif., Aug. 18–20). IEEE, New York, 1997, 173–177.

5. Lin, T.-H., Sanchez, H., Rofougaran, R., and Kaiser, W. CMOS frontend components for micropower RF wireless systems. In Proceedings of the 1998 International Symposium on Low-Power Electronics and Design (Monterey, Calif., Aug. 10–12). IEEE, New York, 1998, 11–15.

6. Pottie, G. Wireless multiple access adaptive communication techniques. In Encyclopedia of Telecommunications, F. Froelich and A. Kent Eds. Marcel Dekker, Inc., New York, 1999, 1–41.

7. Rappaport, T. Wireless Communications: Principles and Practice. Prentice Hall, Upper Saddle River, N.J., 1996.

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seminar report

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