Border Security Using Wireless Integrated Network Sensors

BORDER SECURITY USING WINS

BORDER SECURITY USING WIRELESS INTEGRATED NETWORK SENSORS (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 wire line sensor and actuator systems. On a local, wide wide-ar -area ea scal scale, e, batt battle lefi fiel eld d situ situat atio ional nal awar awarene eness ss will will prov provid idee pers person onne nell heal health th 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 the user user at low low bit bit rate rate with with low low power power trans transcei ceive vers rs.. Conti Continuo nuous us sens sensor or signa signall 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. WINS WI NS Init Initia iate ted d in 1993 1993 under under Defe Defence nce adva advance nce rese resear arch ch proj projec ectt agen agency cy (DARPA) in US. LWIM (Low power wireless integrated micro sensor) program began in 1995 for further development of WINS sponsored by DARPA. In 1998, WINS NG introd introduced uced for wide wide varit varity y of applic applicati ation. on. The LWIM LWIM projec projectt for multi multihop, hop, selfselfassembled, wireless network algorithms for operating at micro power levels.

Dept of Electronics & Communication

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Figure 1.1 An general picture of Wireless Integrated Network Sensors.


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Figure 1.2 Distributed sensors at Border

Figure shows the distribution of sensors at the border of nation. Different sensors are connected together and also connected to the Gateway. The information sensed at the sensors is communicated to gateway. The sensors are distributed on ground, air and inside the water also. All these sensors are connected using Wireless Network.


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Chapter 2

WINS SYSTEM ARCHITECTURE Convent Convention ional al wirele wireless ss networ networks ks are suppor supported ted by comple complex x protoc protocols ols that 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 conside considerr the requir requireme ement nt to servic servicee dense dense sensor sensor distri distribut bution ionss with with an emphas emphasis is 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 WI NS Mult Multih ihop op Comm Communi unica cati tion on netw networ orks ks permi permitt larg largee power power reduc reducti tion on and the the implementation of dense node distribution. The multihop communication has been shown in the figure 2.1. The figure 1.2 represents the general structure of the wireless integrated network sensors (WINS) arrangement.

Continuous operation Low duty cycle Figure 2.1 The wireless integrated network sensor (WINS) architecture


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Chapter 3

WINS NODE ARCHITECTURE The WINS WINS node archit architect ecture ure (Figur (Figuree 3.1) 3.1) is develop developed ed to enable enable contin continuous uous 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 netwo network rk nodes. nodes. Total Total averag averagee syst system em supply supply curre current ntss must must be less less than than 30 A. Low Low 

power, reliable, and efficient network operation is obtained with intelligent sensor nodes that that incl includ udee sens sensor or sign signal al proce process ssin ing, g, contr control ol,, and and a wire wirele less ss netwo network rk inte interf rface ace.. 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.

Figure 3.1 WINS nodes (shown as disks)

For the particular applications of military security, the WINS sensor systems must operate at low power, sampling at low frequency and with environmental background


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BORDER SECURITY USING WINS limi limite ted d sens sensit itiv ivit ity. y. The The micr micro o powe powerr inte interf rfac acee circ circui uits ts must must samp sample le at dc or low 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 with limit limited ed word word length length.. In parti particul cular, ar, WINS WINS applic applicati ations ons are general generally ly tolera tolerant nt to latency. The WINS node event recognition may be delayed by 10 – 100 msec, or longer.

3.1 Block Diagram of WINS

Figure 3.2 shows the block diagram of o f the wireless integrated network sensor (WINS) . This block diagram shows the working principle of the WINS.


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The above figure 3.3 shows the LWIM-III Node.

3.2 Nodes connection of WINS

Figure 3.4 Node Connections


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BORDER SECURITY USING WINS The above figure shows the node connections deployed in WINS. By the fig it can be seen that several nodes are connected together and also with the Gateway which is used for Conventional Conventional Communication, Communication, Internet Internet Connectivit Connectivity y and Remote Remote Maintenance Maintenance and Re-configurability. This type of architecture will be low cost, consumes low power, multi hop, multiply redundant and reconfigurable.


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Chapter 4

WINS MICRO SENSORS Source Source signals signals (seismic, (seismic, infrared, infrared, acoustic and others) others) all decay in amplitude rapidl rapidly y with with radial radial distan distance ce from from the source source.. To maximi maximize ze detect detection ion range, range, sensor sensor sensitivit sensitivity y must be optimized. optimized. In addition, addition, due to the fundamental fundamental limits limits of background background noise, a maximum detection range exists for any sensor. Thus, it is critical to obtain the greate greatest st sensit sensitivi ivity ty and to develo develop p compact compact sensor sensorss that that may be widely widely distr distribu ibuted ted.. Clearly, micro electromechanical 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. Example Exampless of WINS WINS Micro Micro Seismo Seismomet meter er and infrar infrared ed detect detector or device devicess are shown 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.

Figure 4.1 Thermal infrared detectors


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4.1 REMBASS REMBASS stands for Remotely Monitored Battlefield Sensor System. It is used now a days days in unat unatte tend nded ed grou ground nd sens sensor orss (UGS (UGS). ). Thes Thesee sens sensor orss are are used used in the the applications of seismic-acoustic energy, infrared energy and magnetic field to detect enemy activities in the border.

4.2 Sensor Boards

Figure 4.2 Sensor hardware.

Figure 4.2 shows the different sensor hardware used in WINS for border security. From left to right: (a) Mica2 network node, (b) Mica Sensor Board, (c) Mica Power Board, Board, (d) TWR-I TWR-ISMSM-002 002 Radar Board, Board, and the last last figure figure shows shows all of the boards boards attached together which can be used as Sensor in borders.


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Chapter 5

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 5.1. 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.

Figure 5.1 Nodal distance and Traffic


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Chapter 6

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 6.1 give capacities in each direction measured in kbps.

Figure 6.1 Subnet with line capacities

Figure 6.2 Routing Matrix

In figure 6.2 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 =1/(µc-λ ) Ti = Time delay delay in in sec C

= Capacity of the path in Bps

µ

= Mean packet size in bits

λ

= Mean flow flow in packets/sec.


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BORDER SECURITY USING WINS 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 = λ i / λ λ i = Mean packet flow in path λ = Mean packet flow in subnet

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

Figure 5. WINS Comparator response


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Chapter 7

WINS DIGITAL SIGNAL PROCESSING P ROCESSING 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 analyze analyzerr must must be imple implement mented ed in the WI WINS NS digita digitall signal signal proces processin sing g syste system. m. 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



W power

level. level. So the comple complete te WI WINS NS syste system, m, contai containin ning g contro controlle llerr and wirele wireless ss networ network k interface components, achieves low power operation by maintaining only the micropower components in continuous operation. The WINS spectrum analyzer system, shown in Figure 7.1, contains a set of parallel filters.

Figure 7.1 WINS micropower spectrum analyzer architecture.


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Chapter 8

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 availabl availablee to the micro contro controlle ller. r. The micro contro controlle llerr sends sends a HIGH HIGH signal 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.

Figure 8.1 Comparator plot


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Chapter 9

WINS CHARACTERISTICS & APPLICATIONS



Characteristics: •

Support large numbers of sensor.

•

Dense sensor distributions.

•

Thes Thesee sens sensor or are are also also deve develo lope ped d to suppo upport rt shor shortt dist distan ance ce RF

communication •



Internet access to sensors, controls and processor

Applications: •

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 loca local, l, ente enterp rpri rise se scal scale, e, WI WINS NS will will crea create te a manu manufa fact ctur urin ing g

information service for cost and quality control.


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Chapter 10

DESIGN CONSIDERATION a. Reliability: The system must be reliable so that the probability of failures and

faulty operations must be very less. b. Energy: There are four way in which node consumes energy. •

Choosi sing ng righ rightt sens sensor or for for the the job job can can impr improv ovee the the syst system em Sensing: Choo performance and to consume less power.

•

Computation: The sensor must be chosen so that the speed of computation

can be very fast and less faults. •

Storing: The sensor must have sufficient storage to store the sensed data so

that it can be communicated. •

communica icatin ting g betwee between n sensor sensorss is very very import important ant Communicating: The commun factor when it is used for border security. There must not be any faults during communicating the sensed data between various nodes and the gateway.

The sensor must be design to minimize the likelihood of environment effect of wind, rain, snow etc. The enclosure is manufacture from clear acrylic material. Otherwise the sensor may damage due to weather effects and may give fault results.

Figure 10.1 Enclosure


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10.1 Unanticipated Faulty Behavior: We may experie experience nce severa severall failur failures es as a result result of undetec undetectabl table, e, incorr incorrect ectly ly download program and depleted energy level etc. For example node will detect false event when sensor board is overheated. So this unanticipated faulty behavior must be overcome by using the suitable protection for the sensors or by using the proper enclosure as shown in the figure 10.1.


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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.


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BIBLIOGRAPHY [1] Agre, J., Clare, L., Pottie, G., and Romanov, Romanov, N. Development Development platform platform for selforga organi nizi zing ng

wire wirele less ss

sens sensor or

netw networ orks ks..

Inte Intern rnat atio iona nall

Soci Societ ety y

of

Opti Optica call

Engineering, Bellingham, Wa., 1999, 257–268. [2] Asada, G., Dong, M., Lin, T., Newberg, F., Pottie, G., Marcy, H., and Kaiser, W. Wire Wirele less ss inte integr grat ated ed netwo network rk sens sensor ors: s: LowLow-po powe werr syst system emss on a chip. chip. In Proceedings Proceedings of the 24th IEEE European Solid-State Solid-State Circuits Conference Conference (Den Hague, The Netherlands, Sept. 21–25). Elsevier, 1998, 9–12. [3] Dong, M., Yung, G., and Kaiser, W. Low-power signal processing architectures for network microsensor microsensors. s. In Proceedings Proceedings of the 1997 Internatio International nal Symposium Symposium on Low-Power Low-Power Electronics Electronics and Design (Monterey, (Monterey, Calif., Calif., Aug. 18–20). IEEE, IEEE, New York, 1997, 173–177. [4] Lin, Lin, T.-H., T.-H., Sanchez Sanchez,, H., Rofoug Rofougara aran, n, R., and Kaiser Kaiser,, W. CMOS CMOS fronte frontend nd components components for micropower micropower RF wireless wireless systems. systems. In Proceedings Proceedings of the 1998 Intern Internati ational onal Sympo Symposiu sium m on Low-P Low-Power ower Electr Electroni onics cs and Design Design (Monte (Monterey rey,, Calif., Aug. 10–12). IEEE, New York, 1998, 11–15. [5] Potti Pottie, e, G. Wirel Wireless ess multip multiple le access access adapti adaptive ve commun communica icatio tion n techni techniques ques.. In Encycl Encyclope opedia dia of Telecom Telecommun munica icatio tions, ns, F. Froel Froelich ich and A. Kent Eds. Eds. Marcel Marcel Dekker, Inc., New York, 1999, 1–41. [6] Rappaport, T. Wireless Communications: Principles and Practice. Prentice Hall, Upper Saddle River, N.J., 1996. [7] Sohrabi, K., Gao, J., Ailawadhi, V., and Pottie, G. A self-organizing sensor network. network. In Proceedings Proceedings of the 37th Allerton Allerton Conference on Communicati Communication, on, Control, and Computing (Monticello, Ill., Sept. 27–29). [8] Sohrab Sohrabi, i, K., Manriqu Manriquez, ez, B., and Pottie Pottie,, G. Near-g Near-grou round nd wideban wideband d channe channell measurement measurements. s. In Proceedings Proceedings of the 49th Vehicular Vehicular Technology Conference Conference (Hous (Housto ton, n, May May 16–20 16–20). ). IEEE IEEE,, New New York, York, 1999, 1999, 571– 571–574 574.. Van Van Tree Trees, s, H. Detection, Detection, Estimation Estimation and Modulation Modulation Theory. John Wiley & Sons, Inc., New York, 1968.


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Border Security Using Wireless Integrated Network Sensors

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