An extensive network of pipelines is extensively employed worldwide to carry oil and gas fluids over millions of kilometers. The pipeline channel was constructed to resist environmental dynamic loading conditions to provide safe and reliable oil and gas fluids transportation from the production well sites to depot stations installed at sea coastlines. However, pipeline infrastructure damages such as fractures, cracks, leakages, etc., are significant sources of economic losses in pipeline operations. Moreover, pipeline failures can cause considerable ecological catastrophes, human deaths, and financial loss. Important research initiatives have been committed to establishing pipeline breach detection and localization using various techniques to avoid these threats and maintain a secure and dependable pipeline network. This paper reviews different state-of-the-art damage detection methods and their recent advancement with a case study explaining the application of light detection and ranging for pipeline damage detection. The pros and cons of diverse damage detection methods in pipeline networks are also discussed. Research gaps for pipeline damage detection systems are also provided for better understanding and future research.
RehmanKNawazF.Remote pipeline monitoring using wireless sensor networks. In: International conference on communication, computing and digital systems (C-CODE), Islamabad, Pakistan, 2017, pp. 32–37.
2.
BoazLKaijageSSindeR. An overview of pipeline leak detection and location systems. In: Proceedings of the 2nd Pan African international conference on science, computing and telecommunications (PACT 2014), Arusha, Tanzania, 2014, pp. 133–137.
3.
XiaoQLiJSunJ, et al. Natural-gas pipeline leak location using variational mode decomposition analysis and cross-frequency spectrum. Measurement2018; 124: 163–172.
4.
CramerRTulalianRAngeloP, et al. Detecting and correcting pipeline leaks before they become a big problem. In: SPE middle east oil and gas show and conference, Manama, Bahrain, 2015.
5.
JiaZWangZSunW, et al. Pipeline leakage localization based on distributed FBG hoop strain measurements and support vector machine. Optik2019; 176: 1–13.
6.
AjaoLAAdedokunEANwishieyiCP, et al. An anti-theft oil pipeline vandalism detection: embedded system development. Int J Eng Sci Appl2018; 2(2): 55–64.
7.
BrunKKurzR (eds). Compression machinery for oil and gas. Spring City, PA: Gulf Professional Publishing, 2018.
8.
ArifinBMSLiZShahSL, et al. A novel data-driven leak detection and localization algorithm using the Kantorovich distance. Comput Chem Eng2018; 108: 300–313.
9.
MokhatabSPoeWAMakJY. Handbook of natural gas transmission and processing: principles and practices. Spring City, PA: Gulf Professional Publishing, 2018.
10.
LiuJYaoJGallaherM, et al. Study on methane emission reduction potential in China’s oil and natural gas industry. Technical Report, 2008. Research Triangle Park, NC: RTI International.
11.
LenaVG. Pipelines explained: how safe are America’s 2.5 million miles of pipelines. New York, NY: ProPublica2012.
12.
BolotinaIBorikovVIvanovaV, et al. Application of phased antenna arrays for pipeline leak detection. J Pet Sci Eng2018; 161: 497–505.
13.
MengLYuxingLWuchangW, et al. Experimental study on leak detection and location for the gas pipeline based on acoustic method. J Loss Prev Process Ind2012; 25(1): 90–102.
14.
JinHZhangLLiangW, et al. Integrated leakage detection and localization model for gas pipelines based on the acoustic wave method. J Loss Prev Process Ind2014; 27: 74–88.
15.
MahmutogluYTurkK. A passive acoustic based system to locate leak hole in underwater natural gas pipelines. Digital Signal Process2018; 76: 59–65.
16.
LimKWongLChiuWK, et al. Distributed fiber optic sensors for monitoring pressure and stiffness changes in out-of-round pipes. Struct Control Health Monit2016; 23(2): 303–314.
17.
JiaZRenLLiH, et al. Pipeline leak localization based on FBG hoop strain sensors combined with BP neural network. Appl Sci2018; 8(2): 146.
18.
PngWHLinHSPuaCH, et al. Pipeline monitoring and leak detection using Loop integrated Mach-Zehnder Interferometer optical fiber sensor. Opt Fiber Technol2018; 46: 221–225.
19.
NiSHHuangYHLoKFLinDC.Buried pipe detection by ground penetrating radar using the discrete wavelet transform. Computers and Geotechnics2010; 37(4): 440–448.
20.
HoarauQGinolhacGAttoAM, et al. Robust adaptive detection of buried pipes using GPR. Signal Process2017; 132: 293–305.
21.
LiHXiaoDYZhaoX. Morphological filtering assisted field-pipeline small leakage detection. In: 2009 IEEE international conference on systems, man and cybernetics, San Antonio, TX, USA, 2009, pp. 3769–3774.
22.
DelgadoMRMendozaOB. A comparison between leak location methods based on the negative pressure wave. In: 14th international conference on electrical engineering, computing science and automatic control (CCE), Mexico City, Mexico, 2017, pp. 1–6.
23.
ChenQShenGJiangJ, et al. Effect of rubber washers on leak location for assembled pressurized liquid pipeline based on negative pressure wave method. Process Saf Environ Prot2018; 119: 181–190.
24.
KamSI. Mechanistic modeling of pipeline leak detection at fixed inlet rate. J Pet Sci Eng2010; 70(3–4): 145–156.
25.
TianSDuJShaoS, et al. A study on a real-time leak detection method for pressurized liquid refrigerant pipeline based on pressure and flow rate. Appl Therm Eng2016; 95: 462–470.
26.
HeGLiangYLiY, et al. A method for simulating the entire leaking process and calculating the liquid leakage volume of a damaged pressurized pipeline. J Hazard Mater2017; 332: 19–32.
27.
YangZFanSXiongT. Simulation and numerical calculation on pipeline leakage process. In: 2nd international symposium on information engineering and electronic commerce, Ternopil, Ukraine, 2010, pp. 1–5.
28.
XinhongLGuomingCRenrenZ, et al. Simulation and assessment of underwater gas release and dispersion from subsea gas pipelines leak. Process Saf Environ Prot2018; 119: 46–57.
29.
WanJYuYWuY, et al. Hierarchical leak detection and localization method in natural gas pipeline monitoring sensor networks. Sensors2011; 12(1): 189–214.
30.
ManekiyaMHArulmozhivarmanP.Leakage detection and estimation using IR thermography.In: International conference on communication and signal processing (ICCSP), Melmaruvathur, India, 2016, pp. 1516–1519.
31.
SunJPengZWenJ. Leakage aperture recognition based on ensemble local mean decomposition and sparse representation for classification of natural gas pipeline. Measurement2017; 108: 91–100.
32.
GaoYLiuYMaY, et al. Application of the differentiation process into the correlation-based leak detection in urban pipeline networks. Mech Syst Signal Process2018; 112: 251–264.
33.
YinSWengYSongZ, et al. Mass transfer characteristics of pipeline leak-before-break in a nuclear power station. Appl Therm Eng2018; 142: 194–202.
34.
TurnerNC.Hardware and software techniques for pipeline integrity and leak detection monitoring. In: Paper presented at the SPE Offshore Europe, Aberdeen, United Kingdom, 1991.
35.
MurvayPSSileaI. A survey on gas leak detection and localization techniques. J Loss Prev Process Ind2012; 25(6): 966–973.
36.
ZhangJ. Designing a cost-effective and reliable pipeline leak-detection system. Pipes Pipelines Int1997; 42(1): 20–26.
37.
GeigerGVogtDTetznerR. State-of-the-art in leak detection and localization. Oil Gas Eur Mag2006; 32(4): 193.
38.
GolmohamadiM.Pipeline leak detection. Master of Science in Computer Engineering Thesis, Missouri, USA, 2015.
39.
ScottSLBarrufetMA. Worldwide assessment of industry leak detection capabilities for single and multiphase pipelines. College Station, TX: Offshore Technology Research Center, 2003.
40.
MatosSPowellDDaviesR, et al. A guideline framework for the integrity assessment of offshore pipelines. Technical Report, Det Norske Veritas, 2006.
41.
FolgaSM. Natural gas pipeline technology overview. Technical Report ANL/EVS/TM/08-5, Argonne National Laboratory, 2007.
42.
WangXJSimpsonARLambertMF, et al. Leak detection in pipeline systems using hydraulic methods: a review. In: Conference on Hydraulics in Civil Engineering, The Institutuion of Engineers, Australia, Hobart;2001November;pp. 23–30.
43.
SharmaVBSinghKGuptaR, et al. Review of structural health monitoring techniques in pipeline and wind turbine industries. Appl Syst Innovation2021; 4: 59.
44.
AdegboyeMAFungWKKarnikA. Recent advances In pipeline monitoring and oil leakage detection technologies: principles and approaches. Sensors201919(11): 2548.
45.
MartiniATroncossiMRivolaA. Leak detection in water-filled small-diameter polyethylene pipes by means of acoustic emission measurements. Appl Sci2016; 7(1): 2.
46.
LiSWenYLiP, et al. Leak detection and location for gas pipelines using acoustic emission sensors. In: Proceedings of the 2012 IEEE international ultrasonics symposium (IUS), Dresden, Germany, 7–10 October 2012, pp. 957–960. Piscataway, NJ: IEEE.
47.
NaranjoEBaligaS. Expanding the use of ultrasonic gas leak detectors: a review of gas release characteristics for adequate detection. Int Gases Instrum2009; 3(6): 24–29.
48.
ChatzigeorgiouDYoucef-ToumiKBen-MansourR. Design of a novel in-pipe reliable leak detector. IEEE/ASME Trans Mechatron2014; 20(2): 824–833.
49.
FuchsHVRiehleR. Ten years of experience with leak detection by acoustic signal analysis. Appl Acoust1991; 33(1): 1–19.
50.
DavoodiSMostafapourA. Theorical analysis of leakage in high pressure pipe using acoustic emission method. Adv Mater Res2012; 445: 917–922.
51.
DattaSSarkarS. A review on different pipeline fault detection methods. J Loss Prev Process Ind2016; 41: 97–106.
52.
OhSWYoonDBKimGJ, et al. Acoustic data condensation to enhance pipeline leak detection. Nucl Eng Des2018; 327: 198–211.
53.
MillerRKPollockAAWattsDJ, et al. A reference standard for the development of acoustic emission pipeline leak detection techniques. NDT & E Int1999; 32(1): 1–8.
54.
LiSSongYZhouG. Leak detection of water distribution pipeline subject to failure of socket joint based on acoustic emission and pattern recognition. Measurement2018; 115: 39–44.
55.
AiCZhaoHMaR, et al. Pipeline damage and leak detection based on sound spectrum LPCC and HMM. In: Sixth international conference on intelligent systems design and applications, Jian, China, 2006, pp. 829–833.
56.
ZhigangCYidongXMeixiaY, et al. Weak feature signal extraction for small leakage in pipelines based on wavelet. In Proceedings of the IET International Conference on Information Science and Control Engineering 2012 (ICISCE 2012), Shenzhen, China, 7–9 December 2012
57.
LeungCKYWanKTChenL.A novel optical fiber sensor for steel corrosion in concrete structures. Sensors2008; 8: 1960–1976.
58.
SelkerJSThevenazLHuwaldH, et al. Distributed fiber-optic temperature sensing for hydrologic systems. Water Resour Res2006; 42(12): W12202.
59.
FengRBeckJRHallDM, et al. Effects of CO2 and H2S on corrosion of martensitic steels in brines at low temperature. Corrosion2018; 74: 276–287.
60.
AhadiMBakhtiarMS. Leak detection in water-filled plastic pipes through the application of tuned wavelet transforms to acoustic emission signals. Appl Acoust2010; 71(7): 634–639.
61.
YazdekhastiSPiratlaKRAtamturkturS, et al. Novel vibration-based technique for detecting water pipeline leakage. Struct Infrastruct Eng2017;13(6): 731–742.
XuJYangDQinC, et al. Study and test of a new bundle-structure riser stress monitoring sensor based on FBG. Sensors2015; 15: 29648–29660.
64.
El-ZahabSAbdelkaderEMZayedT. An accelerometer-based leak detection system. Mech Syst Signal Process2018; 108: 276–291.
65.
YazdekhastiSPiratlaKRAtamturkturS, et al. Experimental evaluation of a vibration-based leak detection technique for water pipelines. Struct Infrastruct Eng2018; 14(1): 46–55.
66.
MartiniATroncossiMRivolaA. Vibroacoustic measurements for detecting water leaks in buried small-diameter plastic pipes. J Pipeline Syst Eng Pract2017; 8(4): 04017022.
67.
MartiniARivolaATroncossiM. Autocorrelation analysis of vibroacoustic signals measured in a test field for water leak detection. Appl Sci2018; 8(12): 2450.
68.
SrinivasanBVenkiteshD.Distributed fiber-optic sensors and their applications. In: SrinivasanBVenkiteshD (eds) Optical fiber sensors. Boca Raton: CRC Press, 2017
69.
WangLNarasimmanSCRavulaSR, et al. Water ingress detection in low-pressure gas pipelines using distributed temperature sensing system. IEEE Sens J2017; 17(10): 3165–3173.
KhanAAVrabieVMarsJI, et al. A source separation technique for processing of thermometric data from fiber-optic DTS measurements for water leakage identification in dikes. IEEE Sens J2008; 8(7): 1118–1129.
72.
KrollABaetzWPeretzkiD. On autonomous detection of pressured air and gas leaks using passive IR-thermography for mobile robot application. IEEE international conference on robotics and automation, Kobe, Japan, 2009, pp. 921–926.
73.
BensonAK. Applications of ground penetrating radar in assessing some geological hazards: examples of groundwater contamination, faults, cavities. J Appl Geophys1995; 33(1–3): 177–193.
74.
EyubogluSMahdiHAl-ShukriH, et al. Detection of water leaks using ground-penetrating radar. In: Proceedings of the third international conference on applied geophysics, Orlando, FL, USA, 2003, pp. 8–12.
75.
PattersonJECookFA. Successful application of ground-penetrating radar in the exploration of gem tourmaline pegmatites of Southern California. Geophys Prospect2002; 50(2): 107–117.
76.
MaasCSchmalzlJ. Using pattern recognition to automatically localize reflection hyperbolas in data from the ground-penetrating radar. Comput Geosci2013; 58: 116–125.
77.
MoffattDLPuskarRJ. A subsurface electromagnetic pulse radar. Geophysics1976; 41(3): 506–518.
78.
SimiAManacordaGMiniatiM, et al. Underground asset mapping with dual-frequency dual-polarized GPR massive array. In: Proceedings of the XIII international conference on ground penetrating radar, Lecce, Italy, 2010, pp. 1–5.
79.
ZoubirAMChantIJBrownCL, et al. Signal processing techniques for landmine detection using impulse ground-penetrating radar. IEEE Sens J2002; 2: 41–51.
80.
NgWChanTCSoHC, et al. Particle filtering-based approach for landmine detection using ground-penetrating radar. IEEE Trans Geosci Remote Sens2008; 46(11): 3739–3755.
81.
KovalenkoVYarovoyAGLigthartLP. A novel clutter suppression algorithm for landmine detection with GPR. IEEE Trans Geosci Remote Sens2007; 45(11): 3740–3751.
82.
Flores-BolarinJRoyo-PastorR.Infrared thermography: a good tool for nondestructive testing of plastic materials. In: Proceedings of the 5th European thermal-sciences conference, Eindhoven, The Netherlands, 2008, pp. 18–22.
83.
JadinMSGhazaliKH.Gas leakage detection using thermal imaging technique. In: Proceedings of the 2014 UKSim-AMSS 16th international conference on computer modelling and simulation, Cambridge, UK, 26–28 March 2014, pp. 302–306. Piscataway, NJ: IEEE.
84.
ZuoCFengXZhangY, et al. Crack detection in pipelines using multiple electromechanical impedance sensors. Smart Mater Struct2017; 26: 104004.
85.
XuYLuoMLiuQ, et al. PZT transducer array enabled pipeline defect locating based on time-reversal method and matching pursuit de-noising. Smart Mater Struct2019; 28: 075019.
86.
NaWSLeeH. Experimental investigation for an isolation technique on conducting the electromechanical impedance method in high-temperature pipeline facilities. J Sound Vib2016; 383: 210–220.
87.
PetersLPDanielsJJYoungJD. Ground penetrating radar as a subsurface environmental sensing tool. Proc IEEE1994; 82(12): 1802–1822.
88.
BimpasMAmditisAUzunogluN. Detection of water leaks in supply pipes using continuous wave sensor operating at 2.45 GHz. J Appl Geophys2010; 70(3): 226–236.
89.
QuaifeLAckerD.Pipeline leak location technique using a novel test fluid and trained dogs. In: Proceedings of the 5th international conference and exhibition on pipeline pigging and integrity monitoring conference, Houston, TX, USA, 1–4 February 1993.
90.
MandalPC. Gas leak detection in pipelines and repairing system of titas gas. J Appl Eng2014; 2: 23–34.
91.
GarnerKJBusbeeLCornwellP, et al. Duty cycle of the detector dog: a baseline study. Auburn, AL: Institute for Biological Detection Systems, Auburn University, 2001.
92.
ShuklaAKarkiH. Application of robotics in offshore oil and gas industry: a review Part II. Rob Auton Syst2016; 75: 508–524.
93.
CostaMJGonçalvesPMartinsA, et al. Vision-based assisted teleoperation for inspection tasks with a small ROV. In: 2012 Oceans, Hampton Roads, VA, USA, 2012, pp. 1–8.
94.
AnisiDASkourupC. A step-wise approach to oil and gas robotics. IFAC Proc2012; 45(8): 47–52.
95.
GómezCGreenDR. Small unmanned airborne systems to support oil and gas pipeline monitoring and mapping. Arabian J Geosci2017; 10(9): 1–17.
96.
YuXZhangY. Sense and avoid technologies with applications to unmanned aircraft systems: review and prospects. Prog Aerosp Sci2015; 74: 152–166.
97.
McfadyenAMejiasL. A survey of autonomous vision-based see and avoid for unmanned aircraft systems. Prog Aerosp Sci2016; 80: 1–17.
98.
HongjianZPingHXudongY. Fault detection of train center plate bolts loss using modified LBP and optimization algorithm. Open Autom Control Syst J2015; 7(1): 1916–1921.
99.
LiuLZhouFHeY. Automated status inspection of fastening bolts on freight trains using a machine vision approach. Proc IMechE, Part F: J Rail and Rapid Transit2016; 230(7): 1629–1641.
100.
SunJXiaoZ. Potential fault region detection in TFDS images based on convolutional neural network. In: Infrared technology and applications, and robot sensing and advanced control (SPIE), Beijing, China, 2016, pp. 384–391.
101.
NguyenTCHuynhTCRyuJY, et al. Bolt-loosening identification of bolt connections by vision image-based technique. In: Non-destructive characterization and monitoring of advanced materials, aerospace, and civil infrastructure (SPIE), 2016, vol. 9804, pp. 227–243.
102.
ParkJHHuynhTCChoiSH, et al.Vision-based technique for bolt-loosening detection in wind turbine tower. Wind Struct2015; 21(6): 709–726.
103.
WangCWangNHoSC, et al. Design of a new vision-based method for the bolts looseness detection in flange connections. IEEE Trans Ind Electron2019; 67(2): 1366–1375.
104.
OstapkowiczP. Leak detection in liquid transmission pipelines using simplified pressure analysis techniques employing a minimum of standard and non-standard measuring devices. Eng Struct2016; 113: 194–205.
105.
SheltamiTRBalaAShakshukiEM. Wireless sensor networks for leak detection in pipelines: a survey. J Ambient Intell Hum Comput2016; 7: 347–356.
106.
MartinsJCSeleghimP. Assessment of the performance of acoustic and mass balance methods for leak detection in pipelines for transporting liquids. J Fluids Eng2010; 132(1): 011401.
KarimMZAAlrasheedyAGaafarAA.Compensated mass balance method for oil pipeline leakage detection using SCADA. Int J Comput Sci Secur2015; 9: 293–302.
109.
RougierJ. Probabilistic leak detection in pipelines using the mass imbalance approach. J Hydraul Res2005; 43(5): 556–566.
110.
SangYZhangJLuX, et al. Signal processing based on wavelet transform in pipeline leakage detection and location. In: Sixth international conference on intelligent systems design and applications, Jian, China, 2006, pp. 734–739.
111.
YuZJianLZhoumoZ, et al. A combined Kalman filter—discrete wavelet transform method for leakage detection of crude oil pipelines. In: 2009 9th international conference on electronic measurement and instruments, Beijing, China, 2009.
112.
ChenHYeHChenLV, et al. Application of support vector machine learning to leak detection and location in pipelines. In: Proceedings of the 21st IEEE instrumentation and measurement technology conference (IEEE cat. no. 04ch37510), Como, 2004, pp. 2273–2277.
113.
FerranteMBrunoneBMeniconiS. Wavelets for the analysis of transient pressure signals for leak detection. J Hydraul Eng2007; 133(11): 1274–1282.
114.
HouCXZhangEH. Pipeline leak detection based on double sensor negative pressure wave. Appl Mech Mater2013; 313: 1225–1228.
115.
WangHSYeHWangGZ. Oil pipeline leakage detection based on wavelet algorithm. Inf Control Shenyang2002; 31(5): 456–460.
116.
ZhuAJinSZengZ. Application of Kalman filter in pipeline leak detection. Control Instrum Chem Ind2005; 32(5): 57.
117.
PengZWangJHanX. A study of negative pressure wave method based on HAAR wavelet transform in ship piping leakage detection system. In: IEEE 2nd international conference on computing, control and industrial engineering, Wuhan, China, 2011, pp. 111–113.
118.
YonghongSZhenhuaW. Detection of small leakage from pipeline based on improved harmonic wavelet. In: 2012 7th international conference on computer science and education (ICCSE), Melbourne, VIC, Australia, 2012, pp. 45–49.
119.
ChenZLianXYuZ. Leakage detection for oil pipelines based on Independent Component Analysis. In: Proceedings of the 29th Chinese control conference, Beijing, China, 2010, pp. 4009–4013.
120.
LiQLiMZhangX, et al. Research on mixed signal separation method for pipeline leakage based on RobustICA. In: 2016 international conference on robotics and automation engineering (ICRAE), Jeju, South Korea, 2016, pp. 70–74.
121.
NavarroABegovichOSánchezJ, et al. Real time leak isolation based on state estimation with fitting loss coefficient calibration in a plastic pipeline. Asian J Control2017; 19(1): 255–265.
122.
DurgutİLeblebicioğluMK. State estimation of transient flow in gas pipelines by a Kalman filter-based estimator. J Nat Gas Sci Eng2016; 35: 189–196.
123.
GongJCaiJLiX, et al. Research on state estimation of oil pipeline considering adaptive extended Kalman filtering. In: 2007 international conference on mechatronics and automation, Harbin, China, 2007, pp. 1294–1298.
124.
Emara-ShabaikHEKhuliefYAHussainiI. A non-linear multiple-model state estimation scheme for pipeline leak detection and isolation. Proc IMechE, Part I: J Systems and Control Engineering2002; 216(6): 497–512.
125.
Dos SantosPLAzevedo-PerdicoúlisTPJankG, et al. Leakage detection and location in gas pipelines through an LPV identification approach. Commun Nonlinear Sci Numer Simul2011; 16(12): 4657–4665.
126.
TorresLVerdeCBesanconG, et al. High gain observers for leak location in subterranean pipelines of liquefied petroleum gas. Int J Robust Nonlinear Control2014; 24(6): 1127–1141.
127.
NegreteMAVerdeC. Multi-leak reconstruction in pipelines by sliding mode observers. IFAC Proc Vol2012; 45(20): 934–939.
128.
Espinoza-MorenoGBegovichOSanchez-TorresJ.Real time leak detection and isolation in pipelines: a comparison between sliding mode observer and algebraic steady state method. In: 2014 World Automation Congress (WAC), Waikoloa, HI, USA, 2014, pp. 748–753.
129.
VítkovskýJPLambertMFSimpsonAR, et al. Experimental observation and analysis of inverse transients for pipeline leak detection. J Water Resour Plann Manage2007; 133: 519–530.
130.
GiustolisiOSavicDKapelanZ. Pressure-driven demand and leakage simulation for water distribution networks. J Hydraul Eng2008; 134: 626–635.
131.
BerardiLGiustolisiOSavicD, et al. An effective multi-objective approach to prioritisation of sewer pipe inspection. Water Sci Technol2009; 60: 841–850.
132.
ChenYKuoTKaoW, et al. An improved method of soil-gas sampling for pipeline leak detection: flow model analysis and laboratory test. J Nat Gas Sci Eng2017; 42: 226–231.
133.
INTSOK. Russian–Norwegian Oil and Gas Industry Cooperation in the high north. Logistics and Transport-Report, 2013. Oslo, Norway: INTSOK.
134.
DhillonSSChakrabartyK. Sensor placement for effective coverage and surveillance in distributed sensor networks. In: Proceedings of the 2003 IEEE Wireless Communications and Networking (WCNC 2003), New Orleans, LA, USA, 16–20 March 2003, vol. 3, pp. 1609–1614.
135.
AgajoJKoloJGAdegboyeM, et al. Experimental performance evaluation and feasibility study of 6LoWPAN based internet of things. Acta Electrotech Inform2017; 17: 16–22.
136.
LiJAndrewLFohC, et al. Connectivity, coverage and placement in wireless sensor networks. Sensors2009; 9: 7664–7693.
137.
FanGJinS. Coverage problem in wireless sensor network: a survey. J Netw2010; 5: 1033–1042.
138.
Yan-LiWAShi-QanAN. Research on the coverage of wireless sensor network. J Transcluction Technol2005; 2: 25–37.
139.
BoudrigaNHamdiMIyengarS. Coverage assessment and target tracking in 3D domains. Sensors2011; 11: 9904–9927.
140.
AmmariHMDasSK. Integrated coverage and connectivity in wireless sensor networks: a two-dimensional percolation problem. IEEE Trans. Comput2008; 57: 1423–1434.
141.
FanGWangRHuangH, et al. Coverage-guaranteed sensor node deployment strategies for wireless sensor networks. Sensors2010; 10: 2064–2087.
142.
CoskunV. Relocating sensor nodes to maximize cumulative connected coverage in wireless sensor networks. Sensors2008; 8: 2792–2817.
143.
PashazadehSSharifiM. Determining the best sensing coverage for 2-dimensional acoustic target tracking. Sensors2009; 9: 3405–3436.
144.
AbAziz NAMohemmedAWAliasMY.A wireless sensor network coverage optimization algorithm based on particle swarm optimization and Voronoi diagram. In: Proceedings of the 2009 international conference on networking, sensing and control, Okayama, Japan, 26–29 March 2009, pp. 602–607. Piscataway, NJ, USA: IEEE.
145.
PrabhuSBSophiaS. A review of energy efficient clustering algorithm for connecting wireless sensor network fields. Int J Eng Res Technol2013; 2: 477–481.
146.
TianYEkiciE. Cross-layer collaborative in-network processing in multihop wireless sensor networks. IEEE Trans Mobile Comput2007; 6: 297–310.
147.
AnastasiGContiMDi FrancescoM, et al. Energy conservation in wireless sensor networks: a survey. Ad hoc Netw2009; 7: 537–568.
148.
SabatA. Remote monitoring of pipeline using wireless sensor network. Patent 7526994B2, USA, 2009.
149.
PullinRHolfodKMBaxterMG. Modal analysis of acoustic emission signals from artificial and fatigue crack sources in aerospace grade steel. Key Eng Mater2005; 293: 217–226.
150.
MarotiMKusyBBaloghG, et al. Radio interferometric geolocation. In: Proceedings of the third international conference on embedded networked sensor system, Sam Diego, CA, USA, 2–4 November 2005, pp. 1–12.
151.
HimonidesE. Mapping a beautiful voice: the continuous response measurement apparatus (CReMA). J Music Technol Educ2011; 4: 5–25.
152.
HenrieMCarpenterPNicholasRE. Pipeline leak detection handbook. Chennai, India: Joe Hayton, 2016, pp. 1–317.
