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 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.

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Wireless Integrated Network Sensors

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Presented By:

Barbara Theodorides

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WINS
Initiated in 1993 at the UCLA, 1G fielded in 1996
Sponsored by DARPA ? LWIM program began in 1995
In 1998, WINS NG

Distributed network
Internet access to sensors, controls and processors
Low-power signal processing, computation, and low-cost wireless networking
RF communication over short distances ( < 30m )
Applications: Industries, transportation, manufacture, health care, environmental oversight, and safety & security.
A general picture




Concerned about¦
The Physical principles ?dense sensor network
Energy & bandwidth constraints ?distributed & layered signal processing architecture
WINS network architecture
WINS nodes architecture
Physical Principles
When are distributed sensors better?

A. Propagation laws for sensing
All signals decay with distance
e.g. electromagnetic waves in free space (~ 1/d2)
in other media (absorption, scattering, dispersion)





Physical Principles (cont)
What are the fundamental limits driving the design of a network of distributed sensors?

B. Detection & Estimation

Detector: given a set of observables {xj}
determines which of the hypotheses {hi} are true
Target presence/absence: based on estimates parameters {fk} of {xj}
Selected Fourier, wavelet transform coefficients
Marginal improvement

Formally: Decide on hi if p(hi | {fk}) > p(hj | {fk}) ? j ? i

Reliability: #independent observations, SNR
Complexity: dimension of feature space, #hypotheses


Physical Principles (cont)

Use of practical Algorithms:
Apply deconvolution and target-separation machinery to exploit a distributed array (deal with only 1 target and no propagation dispersal effects)
- reduces feature space & #hypotheses
cons: complexity
Deploy a dense sensor network
- homogeneous environment within the detection range
- reduces #environmental features ?size of decision space
attractive method
Physical Principles (cont)
C. Communication Constraints

Spatial separation (e.g. low lying antennas)
Surface roughness, reflecting & obstructing objects
However ? spatial isolation, reuse of frequencies
Multipath propagation (reflections off multiple objects)
Recover ~ space, frequency, and time diversity
But ? for static nodes, time diversity is not an option
? spatial diversity is difficult to obtain
Diversity in frequency domain
Shadowing: dealt with by employing a multihop network



Physical Principles (cont)
D. Energy Consumption

Limits to the energy efficiency of CMOS communications and signal-processing circuits
Limits on the power required to transmit reliably over a given distance






Signal-Processing Architecture
We want: low false-alarm & high detection probability

Processing Hierarchy



Signal-Processing Architecture (cont)
Application Specific

e.g. Remote security application
WINS node: 2 sensors (seismic & imaging capability)
Seismic senor requires little power ? constantly vigilant
Simple energy detection triggers the cameraâ„¢s operation
Collaborative WINS nodes (e.g. target location)
Send image & seismic record to a remote observer

WINS node: simple processing at low power
Radio: does not need to support continuous transmission of images

WINS Network Architecture
Characteristics
Support large numbers of sensor
Low average bit rate communication ( < 1-100 Kbps )
Dense sensor distributions
Exploit the short-distance separation ?multihop communication
Protocols: designed so radios are off ? MAC address should include some variant of time-division access

Time-division protocol
Exchange small messages: performance information, synchronization,
bandwidth reservation requests
Abundant bandwidth ? few conflicts, simple mechanisms

At least one low-power protocol suite has been developed ? feasible to achieve distributed low-power operation in a flat multihop network

WINS Network Architecture (cont)
Link Sensor Network to the Internet
Layering of the protocols (and devices) is needed
WINS Gateways: 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

System Architect “ Responsibilities
Applicationâ„¢s requirements (reduced operation power, improved bit rate, improved bit error rate, reduced cost)
How can Internet protocols (TCP, IPv6) be employed?
- need to conserve energy, unreliability of physical channels
Where should the processing and the storage take place?
- at the source / reducing the amount of data to transmit
WINS Node Architecture
1993: Initiated at the UCLA
1G of field-ready WINS devices and software was fielded (1996)

1995 : DARPA sponsored
- the LWIM project ? multihop, self-assembled, wireless network
algorithms for operating at micropower levels
- the joint, UCLA and Rockwell Science Center of Thousand Oaks,
program ? platform for more sophisticated networking and signal processing algorithms (many types of sensors, less emphasis on power conservation)
Lesson: Separate real-time from higher-level functions

WINS Node Architecture (cont)
1998: WINS NG developed by the authors ? contiguous sensing, signal processing for event detection, local control of actuators, event classification, communication at low power
Event detection is contiguous ? micropower levels
Event detected => alert process to identify the event
Further processing? Alert remote user / neighboring node?
Communication between WINS nodes

WINS Node Architecture (cont)
Further Generations (Future work):
Support plug-in Linux devices
Small, limited sensing devices ? interact with WINS NG nodes in heterogeneous networks
Scavenge energy from the environment ? photocells


Why WINS ?
Low power consumption ( 100 µW average )
Separation of real-time from higher level functions
Hierarchical signal-processing architecture
Application specific
Communication facility ( WINS gateways )
Remote user
Scalable
Reduce amount of data to be send ? scalability to thousands of nodes per gateway

Conclusion
Densely distributed sensor networks (physical constraints)
Layered and heterogeneous processing
Application specific networking architectures
Close intertwining of network processing
Development platforms are now available