Wireless Integrated Network Sensors (WINS) provide a new monitoring and control capability for transportation, manufacturing, healthcare, environmental monitoring and safety and security. Wireless Integrated Network Sensors combine sensing, signal processing, decision capability, and wireless networking capability in a compact, low power system. Using the concept of WINS we can easily identify a stranger or some terrorists entering the border. The border area is divided into a number of nodes. Each node is in contact with each other and with the main node. The noise produced by the footsteps of the stranger is collected using the sensor. This sensed signal is then converted into power spectral density and the compared with a 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 micropower CMOS circuits.
Wireless Integrated Network Sensors (WINS) provide distributed network and Internet access to sensors, controls, and processors that are deeply embedded in equipment, facilities, and the environment. The WINS network is a new monitoring and control capability for applications in transportation, manufacturing, healthcare, environmental monitoring, and safety and security, border security. WINS combine microsensor technology, low power signal processing, low power computation, and low power, low-cost wireless networking capability in a compact system. WINS networks provide sensing, local control, and embedded intelligent systems in structures, materials, and environments. Compact geometry and low cost allow 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 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. The opportunities for WINS depend on the development of scalable, low cost, sensor network architecture. This requires that sensor information to 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.
Wireless Integrated Network Sensors Node Architecture
The WINS node architecture 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 analyzer must all operate at micro power levels. In the event that an event is detected, the spectrum analyzer output may trigger the microcontroller. The microcontroller may then issue commands for additional signal processing operations for identification of the event signal. Protocols for node operation then determine whether a remote user or neighboring 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 30mA. Low power, reliable, and efficient network operation is obtained with intelligent sensor nodes that include sensor signal processing, control, and a wireless network interface. Distributed network sensor devices must continuously monitor multiple sensor systems, process sensor signals, and adapt to changing environments and user requirements while completing decisions on measured signals.
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 micro power 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. In particular, WINS applications are generally tolerant to latency. The WINS node event recognition may be delayed by 10 100 msec, or longer.
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 fig3. 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.
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 fig4. In the figure, the distances between the nodes and the traffic between the nodes have 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.
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 below figure give capacities in each direction measured in kbps.
In fig6 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 =1/(µc-ÃŽ») (1)
Where Ti= Time delay in sec
C= Capacity of the path in Bps
µ= Mean packet size in bits
ÃŽ»= Mean flow in packets/sec.
The mean delay time for the entire subnet is derived from the weighted sum of all the lines. There are different flows to get a 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 = ÃŽ»i / ÃŽ» (2)
Where ÃŽ»i = Mean packet flow in path
ÃŽ» = Mean packet flow in subnet
A series of interface, signal processing, and communication systems have been implemented in micro-power CMOS circuits. A micropower 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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