A Wireless Sensor Network-Based Infrastructure for Real-Time and Online Pipeline Inspection

2966 IEEE SENSORS JOURNAL, VOL. 11, NO. 11, NOVEMBER 2011

A Wireless Sensor Network-Based Infrastructure forReal-Time and Online Pipeline Inspection

Mahmoud Meribout

Abstract—The aim of this paper is to present a secure wirelesssensor network-based infrastructure for fast and accurate detec-tion of eventual leaks that might occur in multiphase pipelines (i.e.,pipelines which carry simultaneously more than one fluid). Thesystem is scalable to monitor long distances of pipelines. It consistsof a newly designed low-cost pipeline set which is composed ofan inner pipe that carries the multiphase fluid, surrounded by asecond outer pipe that holds the leak detection unit. This latestcomprises an air-ultrasonic sensor which continuously senses thepresence of the leak. The location of the leak is determined by abidirectional microphone. Both these sensors are interfaced to awireless sensor module which performs control, signal processing,and transmission tasks. Hence, the second contribution of thispaper is to provide a new secure and reliable communicationprotocol that takes into consideration the nature of the networkin terms of packets patterns and hardware constraints of thecommunicating nodes. Online tests in a laboratory scale flow loopindicate that the system is capable to accurately determine thelocation of the leak and its rate (in 1/min) in fast response time fordifferent scenarios of leaks.

Index Terms—Leak detection, security protocol, ultrasonicsensor, wireless sensor network.

I. INTRODUCTION

F OR oil companies, pipeline leaks are one of the majorcauses of failure because of their significant length in re-

mote and harsh areas causing human monitoring not to be effec-tive. Thus, there is an increasing and urgent need of a methodthat ensures an effective monitoring of these pipelines. This stillremains a challenging task since the operational pipelines aresubject to complex, highly nonlinear temporal and spatial pro-cesses making it difficult to differentiate between faults andstochastic system behaviors. Existing leak detection systemsare mostly acoustic-based systems and rely on the principalthat in case of a leakage at a certain location of the pipe, thepressure values surrounding this point might be found substan-tially different using signal processing techniques. Among var-ious leak detection techniques using this approach, the so calledNegative Pressure Wave (NPW)-based approach [1] has beenwidely adopted for real applications. Its basic principle is thatwhen a leak occurs in a pipeline, an NPW will propagate fromthe leak point towards upstream and downstream ends of the

Manuscript received November 21, 2010; revised April 07, 2011; acceptedMay 05, 2011. Date of publication June 07, 2011; date of current version Oc-tober 26, 2011. The associate editor coordinating the review of this paper andapproving it for publication was Prof. Ralph Etienne-Cummings.

The author is with the Department of Electrical Engineering, PetroleumInstitute, Abu Dhabi 2533, UAE (e-mail: [email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/JSEN.2011.2155054

pipeline leading to a descending of the pressure at each end.Two pressure transducers which are installed at the two ends ofthe pipeline are used to detect and locate the leakage [2], [3].During the past ten years, quite lot of efforts have been made toimprove the performance of leak detection systems based on thiskind of approach, such as [1]–[4]. However, in addition of beingexpensive and difficult to maintain, these systems are intrusivesince the sensors are in direct contact with the fluid. In addition,they can hardly detect small leaks, due to the significant amountof noises caused by multiphase flows. The complex patterns ofacoustic signals which are generated by this type of flow is thecause that only few works have been conducted to detect leaksfor pipes carrying multiphase flows. Thus, it is evident that newand innovative approaches for leak detection are still required.In this paper, a new concept for leak detection for multiphasefluid pipes is presented. It consists of a newly designed low costand modular pipeline set which is composed of an inner pipethat carries the multiphase fluid, surrounded by a second outerpipe that holds the leak detection unit. This latest comprises anair-ultrasonic sensor and a bidirectional microphone which con-tinuously checks the existence of eventual leaks and their loca-tions, respectively. Large amount of works have been suggestedto localize the source of noise using microphones. They consistto deploy an array of microphones at predefined locations andto proceed by using some techniques such as Sum and Delaybeam former (SDBF) [10], [11]. Even though these techniquesare applicable, a much simpler method taking into considerationthe intrinsic characteristics of the application is suggested in thispaper. An example of such characteristics is the one-directionalpropagation of the audio signal inside the pipeline

The two aforementioned sensors are interfaced to a wirelesssensor module which performs data acquisition and signal pro-cessing, control, and transmission tasks. This latest is designedin such a way that the data transfer is secure (in terms of confi-dentiality of messages and their authentication) and reliable (toensure that the leak information reaches the control room forworst case scenarios). Most of the works deployed so far in thisarea were inspired from the tremendous research works done forwireless local area network (LAN) [5], [6]. However, most ofthese works did not lead to an effective solution since the char-acteristics and constraints for wireless LANs are quite differentfrom the ones related to wireless sensor networks. Hence, thesecond contribution of this paper is to provide a new secure andreliable communication protocol that takes into considerationthe intrinsic features of the wireless sensor networks in terms ofpackets patterns and hardware constraints of the communicatingnodes. Online tests on a laboratory-scale flow loop indicate thatthe system is capable to accurately determine the location of

1530-437X/$26.00 © 2011 IEEE

MERIBOUT: A WIRELESS SENSOR NETWORK-BASED INFRASTRUCTURE FOR REAL-TIME AND ONLINE PIPELINE INSPECTION 2967

the leak and its rate (in 1/min) in a fast response time for dif-ferent scenarios of leaks. Furthermore, the performance of thesystem for long pipelines involving hundreds of wireless sensornodes has been evaluated offline using an open source networksimulator. The results indicate that the communication is reli-able under different scenarios. This paper is organized as fol-lows. In Section II, a literature survey on recent techniques forleak detection is presented. This is followed by a description ofdifferent components which compose the proposed system andhow they interact with each other. The wireless sensor moduleand the corresponding algorithm are presented in Sections IIIand IV, respectively. The remaining two last sections addresssome experimental results and conclusion, respectively.

II. LITERATURE SURVEY ON LEAK DETECTION SYSTEMS

A leak detection system using pressure sensors was suggestedin [8] for the detection of gas in the telecommunication trans-mission line which are usually put within a pressurized gasconduits to avoid accumulation of moisture along the cables. Theleak location principal is based on measuring the pressure dropwhich may occur in case of leak and find the best match amongelements stored in a database which is built using an evaluationfunction during the calibration process. This technique wasdemonstrated to be fast and achieved good estimation accuracy.However, the method is applicable only for single phase flows.In [9], a system that uses an expert system employing neuraland probabilistic networks was proposed for the detection ofdielectric fluid leaks. Its principal is based on the observationthat, in general, a qualified and experienced human operator canidentify a leak with data from the pumping plant sensors andcable loading. When a leak occurs, the behavior of the system isdifferent from the typical pattern. The proposed computer-basedleak detection system mimics this approach and has been shownthat under actual field operation, it is capable of continuouslymonitoring the feeder without creating false alarms. However, itis not clear if the system can localize the leak and if it can dealwith multiphase flows. Research on sound localizations has beenextensively conducted for both indoor and outdoor (e.g., [10]).However, most of these systems are either too large for practicalpipeline inspection applications, or involve computationallyexhaustive methods which make them difficult to implement incompact, embedded system modules. In [11], a new real-time3D source localization and classification (between speech, nonspeech, and silence signals) compact system was proposed. Thesystem uses a four-microphone array and uses the time delayestimation method for localization and the decision tree classifierfor classification. The microphones are placed to form triangles.With this technique, the direction-of-arrival (DOA) of the soundsource can be calculated as

(1)

where is the speed of sound in air, is the microphoneseparation distance, and are the propagation distancesfrom the source to each microphone, and is the time delayof arrival for the sound source between both microphones. Theaccuracy of the above method is limited by the fact that theabove equation is valid under the assumption that the triangle’s

Fig. 1. Block diagram of leak detection system.

sides have lengths which are negligible to their distance fromthe source of noise. In [12], a more accurate source localizationis proposed by not using any approximation in their algorithm.The system combines generalized cross correlation method andgeometric triangulation using microphone array to achieve anaccuracy of localization of 1 for angle detection and errorsbelow 4% for distance estimation. However, this techniquecannot be applied for pipeline inspection since in this appli-cation the source of noise and the microphone are located atalmost a same straight line, within the pipeline. In addition,using only microphones around the pipe to detect leaks is notpractical because of the complex signal caused by the multi-phase flow, in addition to noises which might be generated bythe operators such as closing or opening valves. In this paper,the detection of the leak is done with the ultrasonic sensor,whereas its localization is done by searching the different ofphases between the ultrasonic sensor and the microphone.

III. SYSTEM OVERVIEW

Fig. 1 shows the overall network model of the wireless sensornetwork infrastructure. It consists of a cascade of several intelli-gent pipelines which intercommunicate with each other in an adhoc and wireless manner to forward their messages to a remotecomputer via a base station.

Each pipeline segment consists of an inner pipe which carriesthe actual multiphase flow fluid, and an outer pipe which holdsthe electronics of the leak detection unit. This latest is not in di-rect contact with the process fluid and is not subject to any signifi-cant stress. Therefore, the outer pipe can be made of any low-costthin material, while the inner pipe is made with the same materialthan the pipe used to carry the same type of fluid under the sameprocess conditions (same in terms of type of steel/plastic mate-rial and pipeline thickness). Fig. 2 shows the block diagram of apipeline segment. In Fig. 2, the customized flanges are providedto sustain the inner pipe at a specific height to prevent the outerpipe from any stress that might be induced by the inner pipe. Theleak detection unit might be put at any location of the outer pipethat allows it to communicate with the two neighboring pipelinesegments without intermediary hops. For instance, if Zigbee isused as the physical layer protocol, the inner pipe should havea maximal length of up to 300 meters [6] and accordingly themaximal distance between two consecutive leak detection units


Fig. 2. Block diagram of the pipeline module.

Fig. 3. Block diagram of the hardware unit.

should not exceed this value. A U-shaped area is provided un-derneath the leak detection unit to allow the multiphase fluid toaccumulate in case of a leak from the inner pipe occurs. Hence,the wireless sensor module continuously enables the ultrasonictransducer to emit ultrasonic waves towards this U-shaped area.The echoes signal would provide different transit time in case ofleak or not. Another bidirectional microphone is used to sense thenoise caused by the leak inside the pipe in order to determine thelocation of the leak within the actual pipe segment. This latest isdesigned in such a way that a direct gap of air is provided betweentwo consecutive leak detection units to allow RF wireless com-munication between them. This allows our method to be expand-able for offshore pipelines which have the highest probability ofleaks since they are in direct contact with salty water. Hence, anoncorrosive and cheap material for the outer pipe can be pro-vided, making the cause of leak only from the process fluid.

IV. HARDWARE UNIT

The hardware unit (Fig. 3) is modular and consists of a dataacquisition module which provides a digital interface for thewireless sensor module to both the ultrasonic sensor and thebidirectional microphone.

A. Wireless Sensor Module

Fig. 3 shows the block diagram of the wireless sensor module.It is modular and consists of the following:

— a RISC processor to implement high-level application pro-grams such as the software part of the wireless networkprotocol;

Fig. 4. Ultrasonic transducer Interface block diagram.

— a reconfigurable module to perform bit-wise and data-flowoperations which are required for applications such as theproposed security protocol algorithm (see Section IV);

— a radio frequency module, to transfer and receive data in awireless manner;

— an interface module to the ultrasonic transducer.In addition, the board has an embedded simple task scheduler

to allow multiprocessing in round-robin scheme. Both the wire-less sensor module and the associated ultrasonic transducers arehoused in a stainless steel enclosure with IP-68 norm [7] to beprotected against liquids caused by eventual leaks. One of thefeatures of this module is its relatively small size, in addition ofconsuming only small amount of energy.

B. Ultrasonic Transducer Interface Module

Each ultrasonic transducer (Fig. 4) comprises the sensor (air-type ultrasonic sensor) and its corresponding electronics and isprovided with a periodical burst repetition rate of approximately10 Hz for the received echoes to die completely out before anexcitation of 15 V peak-to-peak of the next burst cycle. Thisis fast enough to detect large leaks. Hence, since the air ultra-sonic sensor cannot operate when it is immersed in a liquid, themaximal volumetric flow rate of the leak which can be detectedcan be adjusted by the size of the underneath U-shaped area.The ultrasonic sensor operates in a transmit–receive mode toemit vertically burst of ultrasonic waves of frequency of 40 KHzthrough the air and then collects the received waves and convertthem into electronic signals for further processing by the wire-less sensor board. The returned echoes are preamplified and am-plified with an accumulative gain of up to 30 dB using a vari-able gain amplifier which also provides passband filtering witha bandwidth of . The role of the filter is toreduce low-frequency noises induced by the vibrations of thepipes which are connected to the tank. Thus, using this filter,the signal-to-noise ratio (SNR) of the received signal was im-proved from 4.6 to 16.4 dB which is high enough to performpattern recognition tasks. The output of the filter is connected to


a comparator circuit of the data acquisition module which pro-vides positive digital pulses for filtered voltage greater than 2volts. This digital signal is then provided to the wireless sensorboard for further processing. The built-in temperature sensorin the wireless sensor module allows to perform temperaturecompensation.

C. Bidirectional Micropohone Interface Module

The bidirectional microphone is a piezoresitive sensor, a typeof low-frequency acoustic sensor. It employs the principle ofpiezoresitive effect: When a mechanical stress caused by theleak in the inner pipeline segment occurs in the direction ofsensitivity of the sensor, this latest changes its electrical resis-tance. This sensor is highly sensitive, capable of detecting smallchanges. It is connected to a preamplifier and amplifier circuitwhich amplifies its output signal with a gain of up to 35 dB,while removing high-frequency noises which may be createdby some not leak-related events (e.g., pipe vibration noise, valveoperation) using a low-pass filter having a cutting frequency of 4KHz. The output of this module is connected to a comparator ofthe data acquisition module. This data is received and exploredby the wireless sensor module to determine the location of theleak. In the comparator, the threshold

V. ALGORITHM DESIGN

In the proposed leak detection system, the signals generatedby the ultrasonic sensor and the bidirectional microphone needto be treated in real-time by an embedded pattern recognition al-gorithm which runs on the RISC processor of the wireless sensorkit of each individual pipeline segment. This latest is also re-sponsible to perform packet forwarding to the remote controlroom by taking into consideration the security issues againsteventual threats.

A. Leak Localization Algorithm

The location of the leak can be found as follows: Considera pipeline segment consisting of a bidirectional microphone lo-cated at its middle-length. Let the sound pressure with ra-dian frequency and source amplitude be modeled by

(2)

where is a random initial phase. The sound pressuresensed at a location can be expressed by

(3)

where is the propagation loss (radiating factor) corre-sponding to and is the speed of sound which is about 345m/s and changes slightly with temperature. Hence, the magni-tude of the signal received by the microphone is proportional tothe distance separating it from the leak. Fig. 5 shows a snapshotof the pressure signals captured from the microphone sensorduring one of the experiments before and after processingwith the analog filter. The leak was manually created (usingautomated valves) in the flow loop used in the experiments, at adistance of 0.4 meters from the microphone sensor. Hence, the

Fig. 5. Signal delivered by the bidirectional microphone signal (a) before and(b) after analog filter.

automated valve was slightly opened to allow the fluid passingthrough the pipe to drop over the outer pipe, at the locationjust beneath the valve and to continue flowing until it reachesthe U-shaped area. In Fig. 5, the timestamp 0 correspondsto the time when the automated valve was opened. The rawsignal before filtering clearly shows undesirable high-frequencycomponents caused by vibrations which are induced by thepump used in the flow loop. This same scenario might occurin the field. The filtered signal clearly shows repeated narrowbursts caused primarily by the leak. Hence, the first peak cor-responding to the leak occurred after a delay of approximately1.34 ms, which corresponds barely to the location of the actualleak. Hence, the delay time between the detection of the leakby the ultrasonic sensor and when the leak actually occurs canbe used to determine the location of the leak. This explains thereason why this filtered signal was fed to a comparator of thedata acquisition module (Section III). Note that in this experi-ment, the ultrasonic sensor could detect the leak later than themicrophone sensor (i.e., the leak reached the U-shaped areaafter 3.45 s), which is understandable since the propagation ofsound in the air is much faster than the flow of the fluid in theouter pipe. In general, in case:

— The U-shaped area together with the ultrasonic sensor islocated at distance 0.

— The microphone sensor at distance .— The leak occurs at distance .

Then, the location of the leak ( ) can be determined using thefollowing two equations:

(4)

(5)

where corresponds to the time when the leak actually oc-curred, the time when the leak is detected by the micro-phone, and the time when the leak is detected by the ultra-


sonic sensor. The variable corresponds the speed of the liquidin the outer pipe (i.e., the leak flow rate) which can be calculatedusing the following equation:

(6)

where are the volumes of the fluid in the U-shapedarea at times and , respectively, and the diameter of thecross section of the U-shaped area. Hence, using the above equa-tions, the leak location can be determined using the followingquestion:

(7)

The next section illustrates the hardware algorithm which im-plements the above equation.

B. Leak Detection Algorithm

As was mentioned in Section II, the CPU in the wirelesssensor module periodically enables the ultrasonic transducer totransmit a burst of ultrasonic waves of 40 KHz resonance fre-quency. This task is implemented using an interrupt timer sub-routine which is configured to occur every 100 ms (which cor-responds to 10 Hz frequency). The same subroutine would en-able a second hardware interrupt subroutine corresponding to adigital hardware I/O pin of the CPU which is connected to theoutput of the ultrasonic sensor via the comparator of the dataacquisition card. Hence, afterwards a burst of ultrasonic wavesis initiated, a second timer is enabled. The external hardware in-terrupt corresponding to a raising edge of the signal generatedby the comparator would trigger the second hardware interruptsubroutine which stops the second timer. The number of clocksticks accumulated in this timer is proportional to the distancebetween the ultrasonic transducer and the reflector (or to vol-umes and of the fluid in the U-shaped area at timesand , respectively, see (6). This number of clock ticks alsocorresponds to the term in (7). In the presence of leak, thisvalue is lower than a predefined threshold. The determinationof the term [(7)] is done by enabling a third timer when araising edge occurs on the first peak of the signal generated bythe microphone. In case a noisy spike occurs because of variousreasons other than the leak (e.g., pipeline vibrations or pumps),then this timer is reset after a predefined period of time if no leakis detected. This allows avoiding false alarms by the system.

C. Packet Forwarding Algorithm

In the proposed network infrastructure, the communicationbetween the cluster heads and the nodes is bidirectional. Thisallows the system to handle different situations. For instance,the period of emitting the bursts of ultrasonic waves, as well

Fig. 6. Packet format of vehicles messages.

as the thresholds used for the comparators connecting the ultra-sonic and bidirectional microphones sensors might be remotelyreconfigured from the control room.

The design of the forwarding algorithm needs to take intoconsideration simultaneously the power consumption of indi-vidual nodes as well as the security and authentication of theinformation being transmitted. In the proposed leak detectionsystem, most of the power is consumed during transmissionof packets corresponding to the leak event information orduring private key update. However, the size of the packetscorresponding to these two types of messages are relativelysmall and are relatively not frequent (a reasonable update ofthe private key is 15 min since it would take much longer timefor a hacker to trap its value). Fig. 5 shows the overall formatof the packet. Hence, the source address, Saddr, is the actualaddress of the sink node (which can be the base station or thewireless sensor node corresponding to a pipeline segment).The destination address, Daddr, is the destination address ofthe final node. Both of these two fields are 32 bits width. Thetype field which is 8 bits width indicates the type of messagethat the actual packet is carrying (e.g., key refreshment packet,leak detection, and event). The other fields which are related toother purposes are explained in the next section.

D. Data Authentication and Security Algorithms

In the proposed system, the security and authentication is-sues are tackled using a private key encryption algorithm, whichconsists to periodically update the private keys which are exclu-sively shared between every pair of neighboring nodes. In Fig. 6,the message field contains various types of information such asthe secret key, , of the sender , its confidential physical ad-dress, , and the count value, count ( ) for private key refresh-ment. The authentication code is computed by the sender using(8) shown at the bottom of the page, where is the messagesize. Hence, in order for the intruder to produce the same au-thentication code, it has to know the private key to correctly de-crypt the message field, in addition of knowing the actual valueof count ( ).

(8)


Fig. 7. Key refreshment algorithm.

In the packet forwarding procedure, a high-priority interruptis a configured on the wireless sensor’s CPU (in addition to thethree other interrupts corresponding to the ultrasonic and mi-crophone sensors) to trigger each time a new packet is detectedby the wireless sensor module. This latest might then resendthe same message to the next hop (this corresponds either to aleak detection event packet to be sent to the base station, or toa reconfiguration packet sent by the remote control room to re-configure a given node) or resend some information to the samenode (this corresponds to exchange the private keys with one itstwo neighbor).

Each wireless sensor node is permanently provided with aunique and confidential physical address and an initial con-fidential private key , which should be refreshed (i.e., mod-ified) every 15 min. This would allow a reinforcement of thesecurity of the network, against intruders which may try to cap-ture the key of the actual node and join the network. Hence,the communication between the nodes is performed using a pri-vate key encryption algorithm (i.e., Data Encryption Standard(DES) algorithm). Fig. 7 shows the pseudocode of this algo-rithm. Hence, each wireless sensor node generates a randomcounting number , which is then communicated to itsimmediate neighbor . Next, both the nodes update their key,by computing a function , after exactly clock ticks.In the algorithm, the function depends on the hardware com-plexity of the wireless sensor in order to provide good securitykey in reasonable amount of time. In our case, because a 16 bitsmultiplication can be done in two clock cycles, the followingfunction was implemented:

(9)

where is the number of times the key has been refreshed, andcode is the secret code, initially assigned for the actual node.

VI. EXPERIMENTAL RESULTS

To test the performance of the system in terms of accuracyof measurement and reliability of data transfer under differentintensity of leaks, a laboratory-scale flow loop was designed andimplemented. Fig. 8 shows the 3D perspective of the loop. Thewater might continuously move around the loop at a maximalflow rate of 300 1/min. Three different pipeline segments were

Fig. 8. Single phase flow loop for leak detection testing.

Fig. 9. Response time of the leak detection unit for different intensities of leak.

emulated by inserting manual valves which if open might causea leak into the outer pipeline. The leak detection units are placedunderneath each of the pipeline segments.

Fig. 9 shows the times it takes for the base station to receivethe leak information for different percentage openings of thevalve. Similar experiments for different temperatures were con-ducted and same results were obtained.

From the above, it can be deduced that the system can detectleaks rate of up to 310 ml/s since the leak rate can be obtainedby the formula

(10)

where and are the initial and next measured levels ofwater, and and are the corresponding sampling time. Inaddition the assessment of the accuracy of the system under dif-ferent conditions of leak intensity and temperature indicate thatthe system is at least 95% accurate in terms of providing the ac-tual leak rate in the system.

On the other hand, the power consumption of the leak de-tection unit has been tested for different values of ultrasoundwave transmission rate. The key refreshment rate was kept 15min. Fig. 10 summarizes the results. It can be observed that thepower consumption increase with the increase of the frequencyof transmission of the ultrasound waves. However, the increaseis not significant as the main factor causing the power consump-tion is data transmission.


Fig. 10. Power consumption of the leak detection unit.

VII. CONCLUSION

To deal with the environmental and economical concerns re-garding the multiphase flow leaks in crude oil pipelines, thereis a strong need for an online system that can detect the leakin real-time. Unfortunately, very few works have been done forthis type of fluid, mainly due to the complexity of the problem,while much of the focus was done on single phase flows whichare relatively simpler to handle. Though, even for this case, nowell-established solution is available yet. This paper describesan innovative system, for leak detection in pipelines carryingmultiphase flow fluids. The premise of this work was to developan inexpensive and accurate leak detection method utilizing aminimal number of measured signals. The system is modularand uses air ultrasonic sensors and bidirectional microphonesto detect and localize any eventual leak which might occur ineach single module. In addition, no wiring is required as thecommunication to the remote control room is done using wire-less sensor networks. Hence, a secure wireless routing algorithmwas developed and assessed. Compared to other previous works,the system can naturally handle multiphase flows. In addition,the system was demonstrated to be accurate enough to measurerelatively small amounts of leaks. This leads to claim that thesuggested system might be a good candidate for next genera-tion pipelines.

ACKNOWLEDGMENT

The authors would like to thank the Petroleum Institute forsupporting the present work.

REFERENCES

[1] C. Andberg, J. Holmes, and H. H. Koppitsch, “The application of acontinuous leak detection system pipeline and associated equipment,”IEEE Trans. Ind. Appl., vol. 25, no. 5, pp. 906–909, Sep.-Oct. 1989.

[2] Z. W. Zhang et al., “Leak detection in transport pipelines using en-hanced independent component analysis and support vector machines,”Lecture Notes in Computer Science, vol. 3611, pp. 95–100, Aug. 2005.

[3] J. Yang, Y. Wen, and P. Li, “Leak acoustic detection in water distri-bution pipelines,” in Proc. 7th World Congr. Intell. Control Autom.(WCICA), 2008, pp. 3057–3061.

[4] C. Marques, M. J. Ferreira, and J. G. Rocha, “Leak detection in waterdistribution plastic pipes by spectral analysis of acoustic leak noise,” inProc. 35th Annu. Conf. IEE Ind. Electron., 2009, pp. 2092–2097.

[5] C. Carlof, N. Sastry, and D. Wagner, “Tiny Sec: A Link Layer securityarchitecture for wireless sensor networks,” in Proc. 2nd ACM Conf.Embedded Networked Sensor Syst. (SenSys 2004), Nov. 2004.

[6] G. Guimaraes, E. Souto, D. Sadok, and J. Kelner, “Evaluation of se-curity mechanisms in wireless sensor networks,” Proc. Syst. Commun.Conf., pp. 428–433, Aug. 14–17, 2005.

[7] [Online]. Available: http://www.certifigroup.com/ipcodetesting.html[8] K. Takeda, “Gas leak location method for pressurized cable networks

using pattern matching,” IEEE Trans. Instrum. Meas., vol. 44, no. 3,pp. 764–767, Jun. 1995.

[9] W. Tylman and G. J. Anders, “Novel leak detection system for pipetype cable installations,” IEEE Trans. Power Delivery, vol. 21, no. 3,pp. 1028–1034, Jul. 2006.

[10] J.-M. Valin, F. Michaud, J. Rouat, and D. Letourneau, “Robust soundsource localization using a microphone array on a mobile robot,” inProc. Int. Conf. Intell. Robot. Syst., 2003, vol. 2, pp. 1228–1233.

[11] B. Rudzyn, W. Kadous, and C. Sammut, “Real-time robot auditionsystem incorporating both 3D sound source localisation and voicecharacterization,” in Proc. IEEE Int. Conf. Robot. Autom., Apr. 10–14,2007, pp. 5333–5338.

[12] H. Atmoko, D. C. Tan, G. Y. Tian, and B. Fazenda, “Accurate soundsource localization in a reverberant environment using multipleacoustic sensors,” Meas. Sci. Technol., vol. 19, pp. 1–10, 2008.

Mahmoud Meribout received the Ph.D. degreein electronics engineering from the University ofTechnology of Compiegne, Compiegne, France, inJanuary 3, 1995.

From November 1995 to October 2000, he waswith the NTT Corporation, Japan, and then withthe NEC Corporation, Japan, where he has beeninvolved in several projects related to embeddedsystem design. In November 2000, he joined theDepartment of Electrical and Computer Engineering,Sultan Qaboos University, Muscat, Oman. He then

joined the Department of Electrical Engineering, Petroleum Institute, AbuDhabi, United Arab Emirates, where he is involved in several funded projectsrelated to oil and gas industry. His research interest include embedded systemsdesign and instrumentation.

Documents

A Wireless Sensor Network-Based Infrastructure for Real-Time and Online Pipeline Inspection