Application of Internet of Things and Cloud Computing for Evaluation of Key Performance Indicators for IndianConstruction Projects through Consistent Fuzzy Preference Relations Approach

Main Article Content

Debasis Sarkar
Arpit Solanki

Abstract

The construction industry is considered one of the world’s most important industries. However, it is also one of the most conservative industries when adopting new processes or technologies. The internet of things (IoT) and cloud computing are the latest technologies that have piqued the interest of several industries. This research paper aimed to identify key performance indicators (KPI) of IoT and cloud computing for construction projects and then evaluate the identified KPI through a multi-criteria decision-making tool, namely consistent fuzzy preference relation (CFPR). After evaluation, the CFPR approach was relatively simple to use, faster to execute and more efficient than the conventional analytic hierarchy methods. The method also provided consistent solutions in the decision-making processes. A total of 109 KPI were evaluated using the CFPR approach. The result showed that the nature and willingness of the organisation were the most severe KPI for the application of the IoT and cloud computing for the construction project. The corrective and preventative mitigation measures of the top ten KPI have also been proposed in this study in order to smoothly implement future projects. 

Article Details

How to Cite
Debasis Sarkar, and Arpit Solanki. 2024. “Application of Internet of Things and Cloud Computing for Evaluation of Key Performance Indicators for IndianConstruction Projects through Consistent Fuzzy Preference Relations Approach”. Journal of Construction in Developing Countries 29 (2): 85-115. https://doi.org/10.21315/.
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References

Ahlmeyer, M. and Chircu, A.M. (2016). Securing the internet of things: A review. Issues

in Information Systems, 17(4): 21–28. https://doi.org/10.48009/4_iis_2016_21-28

Alam, M.S.B. (2017). Cloud computing: Architecture, platform and security issues; A

survey. World Scientific News, 86(3): 253–264.

Alhogail, A. (2018). Improving IoT technology adoption through improving consumer

trust. Technologies, 6(3): 2–17. https://doi.org/10.3390/technologies6030064

Ali, H.A.E.M., Al-Sulaihi, I.A. and Al-Gahtani, K.S. (2013). Indicators for measuring

performance of building construction companies in Kingdom of Saudi Arabia.

Journal of King Saud University – Engineering Sciences, 25(2): 125–134. https://

doi.org/10.1016/j.jksues.2012.03.002

Alias, F. and Abdullah, L. (2017). Consistent fuzzy preference relation method and its

application to criteria assessment of quality of life among population of Setiu

Wetlands. Discovering Mathematics, 39(2): 52–60.

Alkhlewi, A., Walters, R. and Wills, G. (2015). Success factors for the implementation

of a private government cloud in Saudi Arabia. In 2015 3rd International

Conference on Future Internet of Things and Cloud. Rome, Italy: IEEE, 387–

https://doi.org/10.1109/FiCloud.2015.81

Al-Mascati, H. and Al-Badi, A.H. (2016). Critical success factors affecting the

adoption of cloud computing in oil and gas industry in Oman. In 2016 3rd

MEC International Conference on Big Data and Smart City (ICBDSC). Muscat,

Oman: IEEE, 187–193. https://doi.org/10.1109/ICBDSC.2016.7460365

Al-Qirim, N. (2011). A roadmap for success in the clouds. In 2011 International

Conference on Innovations in Information Technology. Abu Dhabi, UAE: IEEE,

–275. https://doi.org/10.1109/INNOVATIONS.2011.5893831

Amade, B. and Nwakanma, C.I. (2021). Identifying challenges of internet of things

on construction projects using fuzzy approach. Journal of Engineering, Project

and Production Management, 11(3): 215–227. https://doi.org/10.2478/

jeppm-2021-0021

Anumba, C.J., Akanmu, A., Yuan, X. and Kan, C. (2021). Cyber: Physical systems

development for construction applications. Frontiers of Engineering

Management, 8: 72–87. https://doi.org/10.1007/s42524-020-0130-4

Aripin, I., Zawawi, E. and Ismail, Z. (2019). Factors influencing the implementation

of technologies behind Industry 4.0 in the Malaysian construction industry.

MATEC Web of Conferences, 266: 01006. https://doi.org/10.1051/

matecconf/201926601006

Babu, M.R. (2021). IoT implementation in construction industry (internet of things).

Research Journal of Engineering and Technology, 12(1): 19–25.

Bajpai, A. and Misra, S.C. (2020). Identifying critical risk factors for use of digitalization

in construction industry: A case study. In 2020 IEEE India Council International

Subsections Conference (INDISCON). Visakhapatnam, India: IEEE, 124–128.

https://doi.org/10.1109/INDISCON50162.2020.00036

Baker, B.N., Murphy, D.C. and Fisher, D. (1997). Factors affecting project success. In

D.I. Cleland and W.R. King (eds.), Project Management Handbook. New York:

John Wiley & Sons, 902–919. https://doi.org/10.1002/9780470172353.ch35

Bandyopadhyay, D. and Sen, J. (2011). Internet of things: Applications and challenges

in technology and standardization. Wireless Personal Communications, 58:

–69. https://doi.org/10.1007/s11277-011-0288-5

Banerjee, T. and Sheth, A. (2017). IoT quality control for data and application needs.

IEEE Intelligent Systems, 32(2): 68–73. https://doi.org/10.1109/MIS.2017.35

Bello, S.A., Oyedele, L.O., Akinade, O.O., Bilal, M., Delgado, J.M.D., Akanbi, L.A.,

Ajayi, A.O. and Owolabi, H.A. (2021). Cloud computing in construction

industry: Use cases, benefits and challenges. Automation in Construction, 122:

–18. https://doi.org/10.1016/j.autcon.2020.103441

Chan, F.T., Chong, A.Y.L. and Zhou, L. (2012). An Empirical investigation of factors

affecting e-collaboration diffusion in SMEs. International Journal of Production

Economics, 138(2): 329–344. https://doi.org/10.1016/j.ijpe.2012.04.004

Chang, T.H., Hsu, S.C. and Wang, T.C. (2013). A proposed model for measuring the

aggregative risk degree of implementing an RFID digital campus system with

the consistent fuzzy preference relations. Applied Mathematical Modelling,

(5): 2605–2622. https://doi.org/10.1016/j.apm.2012.06.029

Chang, T.Y. and Chen, Y.T. (2009). Cooperative learning in e-learning: A peer

assessment of student-centered using consistent fuzzy preference. Expert

Systems with Applications, 36(4): 8342–8349. https://doi.org/10.1016/j.

eswa.2008.10.050

Chao, R. (2017). Using consistent fuzzy preference relations for the selection of a

smartphone: A case study of students of a university in Southern Taiwan.

Journal of Gaoyuan, 22: 126–132.

Chao, R.J. and Chen, Y.H. (2009). Evaluation of the criteria and effectiveness

of distance e-learning with consistent fuzzy preference relations. Expert

Systems with Applications, 36(7): 10657–10662. https://doi.org/10.1016/j.

eswa.2009.02.047

Chao, R.J. and Chen, Y.H. (2007). Evaluation of e-learning material design with consistent fuzzy

preference relations. In 2007 IEEE International Fuzzy Systems Conference.

London, UK: IEEE, 1–5. https://doi.org/10.1109/FUZZY.2007.4295432

Chatterjee, S., Kar, A.K. and Gupta, M.P. (2018). Success of IoT in smart cities of

India: An empirical analysis. Government Information Quarterly, 35(3): 349–

https://doi.org/10.1016/j.giq.2018.05.002

Chen, Y., Zhang, H. and Zhang, Y. (2010). Research on the development of internet

of things industry in China. Science and Technology Management Research,

(20): 103–106.

Chiclana, F., Herrera-Viedma, E., Herrera, F. and Alonso, S. (2007). Some induced

ordered weighted averaging operators and their use for solving group

decision-making problems based on fuzzy preference relations. European

Journal of Operation Research, 182(1): 383–399. https://doi.org/10.1016/j.

ejor.2006.08.032

Coughlan, T., Brown, M., Mortier, R., Houghton, R.J., Goulden, M. and Lawson, G.

(2012). Exploring acceptance and consequences of the internet of things

in the home. In 2012 IEEE International Conference on Green Computing

and Communications. Besancon, France: 148–155. https://doi.org/10.1109/

GreenCom.2012.32

Distefano, S., Merlino, G. and Puliafito, A. (2013). Towards the cloud of things:

Sensing and actuation as a service, a key enabler for a new cloud

paradigm. In 2013 Eighth International Conference on P2P, Parallel, Grid,

Cloud and Internet Computing. Compiegne, France: IEEE, 60–67. https://doi.

org/10.1109/3PGCIC.2013.16

Edmondson, V., Cerny, M., Lim, M., Gledson, B., Lockley, S. and Woodward, J. (2018).

A smart sewer asset information model to enable an “internet of things” for

operational wastewater management. Automation in Construction, 91: 193–

https://doi.org/10.1016/j.autcon.2018.03.003

El-Abidi, K.M.A., Ofori, G., Zakaria, S.A.S., Mannan, M.A. and Abas, N.F. (2019).

Identifying and evaluating critical success factors for industrialized building

systems implementation: Malaysia study. Arabian Journal for Science and

Engineering, 44(10): 8761–8777. https://doi.org/10.1007/s13369-019-03941-4

Emam, A.Z. (2013). Critical success factors model for business intelligent over ERP

cloud. In 2013 International Conference on IT Convergence and Security

(ICITCS). Macau, China: IEEE, 1–5. https://doi.org/10.1109/ICITCS.2013.6717819

Ghosh, A., Edwards, D.J. and Hosseini, M.R. (2020). Patterns and trends in internet

of things (IoT) research: Future applications in the construction industry.

Engineering, Construction and Architectural Management, 28(2): 475–481.

https://doi.org/10.1108/ECAM-04-2020-0271

Gupta, S. and Misra, S.C. (2016). Moderating effect of compliance, network and

security on the critical success factors in the implementation of cloud ERP.

IEEE Transactions on Cloud Computing, 4(4): 440–451. https://doi.org/10.1109/

TCC.2016.2617365

Gutierrez, A., Boukrami, E. and Lumsden, R. (2015). Technological, organizational

and environmental factors influencing managers’ decision to adopt cloud

computing in the UK. Journal of Enterprise Information Management, 28(6):

–807. https://doi.org/10.1108/JEIM-01-2015-0001

Hakim, I.M., Singgih, M.L. and Gunarta, I.K. (2021). Critical success factors for

implementation of internet of things (IoT) in automotive companies: A literature

review. In 11th Annual International Conference on Industrial Engineering and

Operations Management. Singapore: IEOM Society International, 5199–5207.

https://doi.org/10.46254/AN11.20210881

Haponava, T. and Al-Jibouri, S. (2012). Proposed system for measuring project

performance using process-based key performance indicators. Journal of

Management in Engineering, 28(2): 140–149. https://doi.org/10.1061/(ASCE)

ME.1943-5479.0000078

Hawash, B., Mokhtar, U.A., Yusof, Z.M., Mukred, M. and Gaid, A.S.A. (2021).

Factors affecting internet of things (IoT) adoption in the Yemeni oil and

gas sector. In 2021 International Conference of Technology, Science and

Administration (ICTSA). Taiz, Yemen: IEEE, 1–7. https://doi.org/10.1109/

ICTSA52017.2021.9406527

Hemanth, G., Sidhartha, C., Jain, S., Saihanish, P. and Rohit, V. (2017). AHP analysis

for using cloud computing in supply chain management in the construction

industry. In 2017 2nd International Conference for Convergence in

Technology (I2CT). Mumbai, India: IEEE, 1228–1233. https://doi.org/10.1109/

I2CT.2017.8226323

Herrera-Viedma, E., Herrera, F., Chiclana, F. and Luque, M. (2004). Some issues on

consistency of fuzzy preference relations. European Journal of Operational

Research, 154(1): 98–109. https://doi.org/10.1016/S0377-2217(02)00725-7

Hsu, C.W. and Yeh, C.C. (2017). Understanding the factors affecting the adoption of

the internet of things. Technology Analysis and Strategic Management, 29(9):

–1102. https://doi.org/10.1080/09537325.2016.1269160

Ibrahim, F.S., Esa, M. and Rahman, R.A. (2021). The adoption of IoT in the Malaysian

construction industry: Towards construction 4.0. International Journal of

Sustainable Construction Engineering and Technology, 12(1): 56–67. http://

doi.org/10.30880/ijscet.2021.12.01.006

Jafarnejad, A., Ebrahimi, M., Abbaszadeh, M.A. and Abtahi, S. (2014). Risk

management in supply chain using consistent fuzzy preference relations.

International Journal of Academic Research in Business and Social Sciences,

(1): 77–90.

Jedermann, R. and Lang, W. (2008). The benefits of embedded intelligence – tasks

and applications for ubiquitous computing in logistics. In C. Floerkemeier, M. Langheinrich, E. Fleisch, F. Mattern and S.E. Sarma (eds.), The Internet of Things

Lecture Notes in Computer Science. Heidelberg: Springer, 105–122. https://

doi.org/10.1007/978-3-540-78731-0_7

Jeong, S., Hou, R., Lynch, J.P., Sohn, H. and Law, K.H. (2019). A scalable cloud-based

cyberinfrastructure platform for bridge monitoring. Structure and Infrastructure

Engineering, 15(1): 82–102. https://doi.org/10.1080/15732479.2018.1500617

Jeyasheeli, P.G. and Selva., J.V.J. (2017). An IoT design for smart lighting in

green buildings based on environmental factors. In 2017 4th International

Conference on Advanced Computing and Communication Systems

(ICACCS). Coimbatore, India: IEEE, 1–5. https://doi.org/10.1109/

ICACCS.2017.8014559

Jianfeng, L., Haifeng, W., Yu, G. and Jianling, H. (2013). Application of multisource information fusion technology in the construction of a secure and emergent transportation platform. In X. Yan, P. Yi, D. Zhu and L. Fu (eds.),

ICTIS 2013: Improving Multimodal Transportation Systems-Information, Safety,

and Integration. Reston, VA: American Society of Civil Engineering, 237–243.

https://doi.org/10.1061/9780784413036.033

Jiao, Y., Wang, Y., Yuan, L. and Li, L. (2012). Cloud and SNS supported collaboration

in AEC industry. In Proceedings of the 2012 IEEE 16th International Conference

on Computer Supported Cooperative Work in Design (CSCWD). Wuhan,

China: IEEE, 842–849. https://doi.org/10.1109/CSCWD.2012.6221919

Jo, J., Jo, B., Kim, J., Kim, S. and Han, W. (2020). Development of an IoT-based

indoor air quality monitoring platform. Journal of Sensors, 1–14. https://doi.

org/10.1155/2020/8749764

Kaushik, S., Srinivasan, K., Sharmila, B., Devasena, D., Suresh, M., Panchal, H.,

Ashokkumar, R., Sadasivuni, K.K. and Srimali, N. (2022). Continuous monitoring

of power consumption in urban buildings based on internet of things.

International Journal of Ambient Energy, 43(1): 1–7. https://doi.org/10.1080/

2021.1931961

Ko, H.S., Azambuja, M. and Lee, H.F. (2016). Cloud-based materials tracking system

prototype integrated with radio frequency identification tagging technology.

Automation in Construction, 63(3): 144–154. https://doi.org/10.1016/j.

autcon.2015.12.011

Kumar, D. and Samalia, H.V. (2016). Investigating factors affecting cloud computing

adoption by SMEs in Himachal Pradesh. In 2016 IEEE International Conference

on Cloud Computing in Emerging Markets (CCEM). Bangalore, India: IEEE,

–16. https://doi.org/10.1109/CCEM.2016.012

Lee, J.Y., Lee, J.W., Cheun, D.W. and Kim, S.D. (2009). A quality model for

evaluating software-as-a-service in cloud computing. In 2009 Seventh ACIS

International Conference on Software Engineering Research, Management

and Applications. Haikou, China: IEEE, 261–266. https://doi.org/10.1109/

SERA.2009.43

Lee, P.C., Xie, W., Lo, T.P., Long, D. and Tang, X. (2019). Cloud model-based

knowledge mapping method for historic building maintenance based

on building information modelling and ontology. KSCE Journal of Civil

Engineering, 23: 3285–3296. https://doi.org/10.1007/s12205-019-2457-0

Li, C.Z., Xue, F., Li, X., Hong, J. and Shen, G.Q. (2018). An internet of things-enabled

BIM platform for on-site assembly services in prefabricated construction.

Automation in Construction, 89(5): 146–161. https://doi.org/10.1016/j.

autcon.2018.01.001

Li, D., Guo, H., Wang, X., Liu, Z., Li, C. and Wang, W. (2016). Analyzing the effectiveness

of policy instruments on new energy vehicle industry using consistent fuzzy

preference relations: A case study in China. International Review for Spatial

Planning and Sustainable Development, 4(3): 45–57. https://doi.org/10.14246/

irspsd.4.3_45

Lin, C.W., Shih-Tong, L.U. and Chung, C. (2011). Key influence factors of containership

registration in Taiwan-application of consistent fuzzy preference relation. In

IEEE International Summer Conference of Asia Pacific Business Innovation

and Technology Management. Dalian, China: IEEE, 289–293. https://doi.

org/10.1109/APBITM.2011.5996341

Lu, S., Lin, C. and Tzeng, G. (2009). Using consistent fuzzy preference relations to risk

factors priority of metropolitan underground project. In Y. Shi, S. Wang, Y. Peng,

J. Li and Y. Zeng (eds.), Cutting-Edge Research Topics on Multiple Criteria

Decision Making. Heidelberg: Springer, 833–839. https://doi.org/10.1007/978-

-642-02298-2_120

Luqman, A. and van Belle, J.P. (2017). Analysis of human factors to the adoption

of internet of things-based services in informal settlements in Cape Town.

In 2017 1st International Conference on Next Generation Computing

Applications (NextComp). Mauritius: IEEE, 61–67. https://doi.org/10.1109/

NEXTCOMP.2017.8016177

Manojkumar, P., Suresh, M., Ahmed, A.A.A., Panchal, H., Rajan, C.A.,

Dheepanchakkravarthy, A., Geetha, A., Gunapriya, B., Mann, S. and

Sadasivuni, K.K. (2022). A novel home automation distributed server

management system using internet of things. International Journal of Ambient

Energy, 43(1): 5478–5483. https://doi.org/10.1080/01430750.2021.1953590

Martín-Garín, A., Millán-García, J.A., Baïri, A., Gabilondo, M. and Rodríguez, A. (2020).

IoT and cloud computing for building energy efficiency. In F. Pacheco-Torgal,

E.S. Rasmussen, C.G. Granqvist, V. Ivanov, A. Kaklauskas and S. Makonin

(eds.), Start-Up Creation: The Smart Eco-Efficient Built Environment. Oxford:

Woodhead Publishing, 235–265. https://doi.org/10.1016/B978-0-12-819946-

00010-2

Martín-Garín, A., Millán-García, J.A., Baïri, A., Millán-Medel, J. and Sala-Lizarraga,

J.M. (2018). Environmental monitoring system based on an open source

platform and the internet of things for a building energy retrofit. Automation

in Construction, 87(3): 201–214. https://doi.org/10.1016/j.autcon.2017.12.017

Misra, D., Das, G., Chakrabortty, T. and Das, D. (2018). An IoT-based waste

management system monitored by cloud. Journal of Material Cycles and

Waste Management, 20(3): 1574–1582. https://doi.org/10.1007/s10163-018-

-y

Nair, K.K., Pillai, M.M., Lefophane, S. and Nair, H.D. (2020). Adaptation of smart cities in

the South African internet of things context. In 2020 International Conference

on Artificial Intelligence, Big Data, Computing and Data Communication

Systems (icABCD). Durban, South Africa: IEEE, 1–6. https://doi.org/10.1109/

icABCD49160.2020.9183832

Naticchia, B., Corneli, A. and Carbonari, A. (2020). Framework based on

building information modeling, mixed reality and a cloud platform to

support information flow in facility management. Frontiers of Engineering

Management, 7: 131–141. https://doi.org/10.1007/s42524-019-0071-y

Ng, S.T., Xu, F.J., Yang, Y., Li, H. and Li, J. (2018). A social networking enabled

crowdsourcing system for integrated infrastructure asset management. In C.

Wang, C. Harper, Y. Lee, R. Harris and C. Berryman (eds.), Construction Research

Congress 2018: Infrastructure and Facility Management. Reston, VA: American

Society of Civil Engineer, 322–331. http://doi.org/10.1061/9780784481295.033

Oke, A.E., Kineber, A.F., Albukhari, I., Othman, I. and Kingsley, C. (2021). Assessment

of cloud computing success factors for sustainable construction industry:

The case of Nigeria. Buildings, 11(2): 1–15. https://doi.org/10.3390/

buildings11020036

Olushola, O.B. (2019). Factors affecting IoT adoption. IOSR Journal of Computer

Engineering, 21(6): 19–24.

Ozdemir, Y., GökhanNalbant, K. and Basligil, H. (2017). Evaluation of personnel

selection criteria using consistent fuzzy preference relations. International

Journal of Management Science, 4(6): 76–81.

Palacios-Marqués, D., Soto-Acosta, P. and Merigó, J.M. (2015). Analyzing the effects

of technological, organizational and competition factors on web knowledge

exchange in SMEs. Telematics and Informatics, 32(1): 23–32. https://doi.

org/10.1016/j.tele.2014.08.003

Patel, D.A., Kikani, K.D. and Jha, K.N. (2016). Hazard assessment using consistent

fuzzy preference relations approach. Journal of Construction Engineering

and Management, 142(12): 1–10. https://doi.org/10.1061/(ASCE)CO.1943-

0001192

Polyviou, A., Pouloudi, N. and Rizou, S. (2014). Which factors affect software-as-aservice selection the most? A study from the customer’s and the vendor’s

perspective. In 2014 47th Hawaii International Conference on System Sciences.

Waikoloa, USA: IEEE, 5059–5068. https://doi.org/10.1109/HICSS.2014.621

Porrawatpreyakorn, N., Nuchitprasitchai, S., Viriyapant, K., Tangprasert, S. and

Chaipunyathat, A. (2019). Understanding key enablers of cloud computing

adoption and acceptance over time. In 2019 Research, Invention, and

Innovation Congress (RI2C). Bangkok, Thailand: IEEE, 1–6. https://doi.

org/10.1109/RI2C48728.2019.8999888

Qing, A.T.(2019). Exploring the adoption of internet of things in Malaysian construction

industry. Final year project. Universiti Tunku Abdul Rahman. Available at:

http://eprints.utar.edu.my/3374/1/QS-2019-1405152-1.pdf [Accessed on 11

February 2022]

Ranganath, N., Sarkar, D., Patel, P. and Patel, S. (2022). Application of fuzzy TOPSIS

method for risk evaluation in development and implementation of solar park

in India. International Journal of Construction Management, 22(14): 2764–

https://doi.org/10.1080/15623599.2020.1826027

Redmond, A., Hore, A., Alshawi, M. and West, R. (2012). Exploring how information

exchanges can be enhanced through Cloud BIM. Automation in Construction,

(7): 175–183. https://doi.org/10.1016/j.autcon.2012.02.003

Sarkar, D. and Singh, M. (2022). Risk analysis by integrated fuzzy expected value

method and fuzzy failure mode and effect analysis for an elevated metro

rail project of Ahmedabad, India. International Journal of Construction

Management, 22(10): 1–12. https://doi.org/10.1080/15623599.2020.1742634

Sarkar, D. and Singh, M. (2021). Development of risk index for mass rapid transit system project in

Western India through application of fuzzy analytical hierarchy process

(FAHP). International Journal of Construction Management, 21(5): 439–451.

https://doi.org/10.1080/15623599.2018.1557997

Sarkar, D., Raghavendra, H.B. and Ruparelia, M. (2015). Role of key performance

indicators for evaluating the usage of BIM as tool for facility management of

construction projects. International Journal of Civil and Structural Engineering,

(4): 370–378.

Saaty, T.L. (1980). The Analytic Hierarchy Process. New York: McGraw-Hill.

Sibiya, M., Aigbavboa, C. and Thwala, W. (2015). Construction projects key

performance indicators: A case of South African construction industry. In Y.

Wang, T. Olofsson, G.Q. Shen and Y. Bai (eds.), ICCREM 2015: Environment and

the Sustainable Building. Reston, VA: American Society of Civil Engineering,

–960. https://doi.org/10.1061/9780784479377.111

Silverio-Fernández, M.A., Renukappa, S. and Suresh, S. (2021). Strategic framework

for implementing smart devices in the construction industry. Construction

Innovation, 21(2): 218–243. https://doi.org/10.1108/CI-11-2019-0132

Singh, M. and Srivastava, V.M. (2018). Multiple regression based cloud adoption

factors for online firms. In 2018 International Conference on Advances in

Computing and Communication Engineering (ICACCE). Paris, France: IEEE,

–152. https://doi.org/10.1109/ICACCE.2018.8457722

Suhanto, A., Hidayanto, A.N., Naisuty, M., Bowo, W.A., Budi, N.F.A. and Phusavat, K.

(2019). Hybrid cloud data integration critical success factors: A case study at

PT Pos Indonesia. In 2019 Fourth International Conference on Informatics and

Computing (ICIC). Semarang, Indonesia: IEEE, 1–6. https://doi.org/10.1109/

ICIC47613.2019.8985748

Tahir, H.M., Kamis, N.H., Ramli, N.A., Bahari, U.T. and Anoar, N.L.M. (2012). Criteria

weights determination in choosing mathematics programme based on

consistent fuzzy preference relations: A case study. In 2012 IEEE Colloquium

on Humanities, Science and Engineering (CHUSER). Sabah, Malaysia: IEEE,

–698. https://doi.org/10.1109/CHUSER.2012.6504401

Tariq, M.I., Tayyaba, S., Rasheed, H. and Ashraf, M.W. (2017). Factors influencing

the cloud computing adoption in higher education institutions of Punjab,

Pakistan. In 2017 International Conference on Communication, Computing

and Digital Systems (C-CODE). Islamabad, Pakistan: IEEE, 179–184. https://doi.

org/10.1109/C-CODE.2017.7918925

Teh, H.Y., Kempa-Liehr, A.W. and Wang, K.I.K. (2020). Sensor data quality: A

systematic review. Journal of Big Data, 7(11): 1–49. https://doi.org/10.1186/

s40537-020-0285-1

Terrada, L., Alloubane, A., Bakkoury, J. and Khaili, M.E. (2018). IoT contribution in

supply chain management for enhancing performance indicators. In 2018

International Conference on Electronics, Control, Optimization and Computer

Science (ICECOCS). Kenitra, Morocco: IEEE, 1–5. https://doi.org/10.1109/

ICECOCS.2018.8610517

Tongsuksai, S., Mathrani, S. and Taskin, N. (2019). Cloud enterprise resource planning

implementation: A systematic literature review of critical success factors.

In 2019 IEEE Asia-Pacific Conference on Computer Science and Data

Engineering (CSDE). Melbourne, Australia: IEEE, 1–8. https://doi.org/10.1109/

CSDE48274.2019.9162373

Tripathi, K.K. and Jha, K.N. (2018). An empirical study on performance measurement

factors for construction organizations. KSCE Journal of Civil Engineering, 22:

–1066. https://doi.org/10.1007/s12205-017-1892-z

Uslu, B.Ç., Okay, E. and Dursun, E. (2020). Analysis of factors affecting IoT-based

smart hospital design. Journal of Cloud Computing, 9(1): 1–23. https://doi.

org/10.1186/s13677-020-00215-5

Wang, J., Lin, X. and Tian, S. (2020). Evaluation of nationwide enterprise-starting

environment based on consistent fuzzy preference relations: A case study

of Daqing City. In Proceedings of the Fifth International Conference on

Economic and Business Management (FEBM 2020). Paris: Atlantis Press, 116–

https://doi.org/10.2991/aebmr.k.201211.023

Wang, T. and Liang, J. (2006). Applying consistent fuzzy preference relations to

measure user perceived service quality of information presenting web

portals. In G.R. Dattatreya (ed.), MATH’06: Proceedings of the 10th WSEAS

International Conference on Applied Mathematics. Wisconsin: World

Scientific and Engineering Academy and Society (WSEAS), 279–285.

Wang, T.C. and Chang, T.H. (2007a). Application of consistent fuzzy preference

relations in predicting the success of knowledge management

implementation. European Journal of Operational Research, 182(3): 1313–

https://doi.org/10.1016/j.ejor.2006.09.039

Wang, T.C. and Chang, T.H. (2007b). Forecasting the probability of successful knowledge management

by consistent fuzzy preference relations. Expert Systems with Applications,

(3): 801–813. https://doi.org/10.1016/j.eswa.2006.01.021

Wang, T.C. and Chen, Y.H. (2007). Applying consistent fuzzy preference relations to

partnership selection. Omega International Journal of Management Science,

(4): 384–388. https://doi.org/10.1016/j.omega.2005.07.007

Wang, T.C. and Lin, Y.L. (2009). Applying the consistent fuzzy preference relations to

select merger strategy for commercial banks in new financial environments.

Expert Systems with Applications, 36(3): 7019–7026. https://doi.org/10.1016/j.

eswa.2008.08.023

Wang, T.C. and Lin, Y.L. (2008). Applying consistent fuzzy preference relation to select merger

strategy for financial organizations. In 2008 International Conference on

Machine Learning and Cybernetics. Kunming, China: IEEE, 222–227. https://

doi.org/10.1109/ICMLC.2008.4620408

Wang, X. (2015). The trend of urban freight regulatory path under the cloud. In Q. Peng,

K.C.P. Wang, X. Liu and B. Chen (eds.), ICTE 2015. Reston, VA: American Society

of Civil Engineering, 2102–2108. https://doi.org/10.1061/9780784479384.268

Wang, X. and Sun, W. (2020). Application and prospect of new technology in

inland waterway regulation. In X. Liu, Q. Peng and K.C.P. Wang (eds.), ICTE

Reston, VA: American Society of Civil Engineering, 208–216. https://doi.

org/10.1061/9780784482742

Wang, Y. and Yeo, G.T. (2019). Transshipment hub port selection for shipping carriers

in a dual hub-port system. Maritime Policy and Management, 46(6): 701–714.

https://doi.org/10.1080/03088839.2019.1627012

Wei, C. and Li, Y. (2011). Design of energy consumption monitoring and energysaving management system of intelligent building based on the internet of

things. In 2011 International Conference on Electronics, Communications and

Control (ICECC). Ningbo, China: IEEE, 3650–3652. https://doi.org/10.1109/

ICECC.2011.6066758

Whitmore, A., Agarwal, A. and Xu, L.D. (2015). The internet of things: A survey of

topics and trends. Information Systems Frontiers, 16(1): 261–274. https://doi.

org/10.1007/s10796-014-9489-2

Xu, G., Li, M., Chen, C.H. and Wei, Y. (2018). Cloud asset-enabled integrated IoT

platform for lean prefabricated construction. Automation in Construction,

(9): 123–134. https://doi.org/10.1016/j.autcon.2018.05.012

Xu, L.D., He, W. and Li, S. (2014). Internet of things in industries: A survey. IEEE

Transactions on Industrial Informatics, 10(4): 2233–2243. https://doi.

org/10.1109/TII.2014.2300753

Yang, S.J. and Liao, C.H. (2017). A study of critical success factors on software quality

assurance of cloud networking devices. In 2016 3rd International Conference

on Systems and Informatics (ICSAI). Shanghai, China: IEEE, 762–767. https://

doi.org/10.1109/ICSAI.2016.7811054

Zaslavsky, A. and Jayaraman, P. (2015). The internet of things: Discovery in the

internet of things. Ubiquity, 10: 1–10. http://doi.org/10.1145/2822529

Zhang, H., Yan, X., Li, H., Jin, R. and Fu, H. (2019). Real-time alarming, monitoring

and locating for non-hard-hat use in construction. Journal of Construction

Engineering and Management, 145(3): 1–13. https://doi.org/10.1061/(ASCE)

CO.1943-7862.0001629

Zhao, Y.L., Tang, J., Huang, H.P., Wang, Z., Chen, T.L., Chiang, C.W. and Chiang,

P.C. (2020). Development of IoT technologies for air pollution prevention and

improvement. Aerosol Air Quality Research, 20(12): 2874–2888. https://doi.

org/10.4209/aaqr.2020.05.0255

Zhihong, F. (2020). Application of IoT technology in construction engineering safety

management. In 2020 International Conference on Urban Engineering and

Management Science (ICUEMS). Zhuhai, China: IEEE, 651–656. https://doi.

org/10.1109/ICUEMS50872.2020.00143