Abstract: (3155 Views)
Background and Purpose: The IoT is recognized as one of the most efficient and pervasive technologies that is constantly evolving. In order to use it effectively, it is necessary to get acquainted with the capabilities of this technology and the importance of each of them. Therefore, this study was conducted with the aim of identifying and ranking the capabilities of the Internet of Things in the industrial sector using multi-criteria decision-making techniques. And quantitative-qualitative research in terms of data analysis.
Materials and methods: In this study, IoT capabilities were identified in three categories of capabilities, benefits and challenges using library resources and Delphi method through a survey of experts. Data collection was done through questionnaires. Expert Choice software was performed.
Findings: The results of data analysis in this study showed that among the three main criteria, obstacles and challenges, advantages and capabilities are the most important, respectively. Also, among the sub-criteria of obstacles and challenges, security and operating system were the most important and compatibility was the least important. Among the sub-criteria of capabilities, artificial intelligence and communication had the highest and sensors the lowest and weighted rank. Also, among the benefits, saving time and reducing costs were the most important, and process improvement was the least important.
Conclusion: The results of this study showed that in order to use technologies such as the Internet of Things in the manufacturing sector, including the industrial sector, in order to use them more effectively and efficiently, it is necessary to identify the capabilities, advantages and obstacles of this technology. By determining the degree of importance and effectiveness of each of these criteria, selecting and prioritizing that aspect of technology for implementation is determined. Therefore, the results of this study, in addition to identifying the capabilities, advantages and obstacles of using this technology, also identified the priority of each criterion in terms of their importance.
Materials and methods: In this study, IoT capabilities were identified in three categories of capabilities, benefits and challenges using library resources and Delphi method through a survey of experts. Data collection was done through questionnaires. Expert Choice software was performed.
Findings: The results of data analysis in this study showed that among the three main criteria, obstacles and challenges, advantages and capabilities are the most important, respectively. Also, among the sub-criteria of obstacles and challenges, security and operating system were the most important and compatibility was the least important. Among the sub-criteria of capabilities, artificial intelligence and communication had the highest and sensors the lowest and weighted rank. Also, among the benefits, saving time and reducing costs were the most important, and process improvement was the least important.
Conclusion: The results of this study showed that in order to use technologies such as the Internet of Things in the manufacturing sector, including the industrial sector, in order to use them more effectively and efficiently, it is necessary to identify the capabilities, advantages and obstacles of this technology. By determining the degree of importance and effectiveness of each of these criteria, selecting and prioritizing that aspect of technology for implementation is determined. Therefore, the results of this study, in addition to identifying the capabilities, advantages and obstacles of using this technology, also identified the priority of each criterion in terms of their importance.
References
1. Abdel-Basset, M., Manogaran, G., Mohamed, M., & Rushdy, E. (2019). Internet of things in smart education environment: Supportive framework in the decision-making process [10.1002/cpe.4515]. Concurrency and Computation: Practice and Experience, 31(10), e4515. 10.1002/cpe.4515 []
2. Abed, S., Alyahya, N., & Altameem, A. (2020). IoT in Education: Its Impacts and Its Future in Saudi Universities and Educational Environments. In (pp. 47-62). [DOI:10.1007/978-981-15-0029-9_5]
3. Asseo, I., Johnson, M., Nilsson, B., Chalapathy, N., & Costello, T. J. (2016). The Internet of things: Riding the wave in higher education. EDUCAUSE review, 51(4), 11-33.
4. Banica, L., Burtescu, E., & Enescu, F. (2017). The impact of internet-of-things in higher education. Scientific Bulletin-Economic Sciences, 16(1), 53-59.
5. Čolaković, A., & Hadžialić, M. (2018). Internet of Things (IoT): A review of enabling technologies, challenges, and open research issues. Computer Networks, 144, 17-39. [DOI:https://doi.org/10.1016/j.comnet.2018.07.017]
6. Elazhary, H. (2019). Internet of Things (IoT), mobile cloud, cloudlet, mobile IoT, IoT cloud, fog, mobile edge, and edge emerging computing paradigms: Disambiguation and research directions. Journal of Network and Computer Applications, 128, 105-140. [DOI:https://doi.org/10.1016/j.jnca.2018.10.021]
7. Espada, J. P., Yager, R. R., & Guo, B. (2014). Internet of things: Smart things network and communication. Journal of Network and Computer Applications, 42, 118-119. [DOI:https://doi.org/10.1016/j.jnca.2014.03.003]
8. Francisti, J., Balogh, Z., Reichel, J., Magdin, M., Koprda, Š., & Molnár, G. (2020). Application Experiences Using IoT Devices in Education. Applied Sciences, 10(20). [DOI:10.3390/app10207286]
9. Kahlert, M. (2016a). Understanding customer acceptance of Internet of Things services in retailing : an empirical study about the moderating effect of degree of technological autonomy and shopping motivations. In.
10. Kahlert, M. (2016b). Understanding customer acceptance of Internet of Things services in retailing: an empirical study about the moderating effect of degree of technological autonomy and shopping motivations.
11. Luo, E., Bhuiyan, M. Z. A., Wang, G., Rahman, M. A., Wu, J., & Atiquzzaman, M. (2018). PrivacyProtector: Privacy-Protected Patient Data Collection in IoT-Based Healthcare Systems. IEEE Communications Magazine, 56(2), 163-168. [DOI:10.1109/MCOM.2018.1700364]
12. Majeed, A., & Ali, M. (2018, 8-10 Jan. 2018). How Internet-of-Things (IoT) making the university campuses smart? QA higher education (QAHE) perspective. 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC),
13. McGeary, J. (2009). A critique of using the Delphi technique for assessing evaluation capability-building needs. Evaluation Journal of Australasia, 9(1), 31-39.
14. Mekki, K., Bajic, E., Chaxel, F., & Meyer, F. (2019). A comparative study of LPWAN technologies for large-scale IoT deployment. ICT express, 5(1), 1-7.
15. Mircea, M., Stoica, M., & Ghilic-Micu, B. (2021). Investigating the Impact of the Internet of Things in Higher Education Environment. IEEE Access, 9, 33396-33409. [DOI:10.1109/ACCESS.2021.3060964]
16. Mohammadzadeh, A. K., Ghafoori, S., Mohammadian, A., Mohammadkazemi, R., Mahbanooei, B., & Ghasemi, R. (2018). A Fuzzy Analytic Network Process (FANP) approach for prioritizing internet of things challenges in Iran. Technology in Society, 53, 124-134. [DOI:https://doi.org/10.1016/j.techsoc.2018.01.007]
17. Orosz, B., Kovács, C., Karuović, D., Molnár, G., Major, L., Vass, V., Szűts, Z., & Námesztovszki, Z. (2019). Digital education in digital cooperative environments. Journal of Applied Technical and Educational Sciences, 9(4), 55-69. [DOI:10.24368/jates.v9i4.149]
18. Oza, S., Ambre, A., Kanole, S., Kshirsagar, P., Dhabekar, N., Paliwal, K., & Hendre, V. (2020, 2020//). IoT: The Future for Quality of Services. ICCCE 2019, Singapore.
19. Rahim, M. A., Rahman, M. A., Rahman, M. M., Asyhari, A. T., Bhuiyan, M. Z. A., & Ramasamy, D. (2021). Evolution of IoT-enabled connectivity and applications in automotive industry: A review. Vehicular Communications, 27, 100285. [DOI:https://doi.org/10.1016/j.vehcom.2020.100285]
20. Rayes, A., & Salam, S. (2017). Internet of Things (IoT) Overview. In A. Rayes & S. Salam (Eds.), Internet of Things From Hype to Reality: The Road to Digitization (pp. 1-34). Springer International Publishing. [DOI:10.1007/978-3-319-44860-2_1]
21. Saaty, T. L. (1990). The Analytic Hierarchy Process. Planning, Priority Setting, Resouce Allocation. In: RWS Publications. University of Pittsburgh.
22. Sharma, S., & Kaushik, B. (2019). A survey on internet of vehicles: Applications, security issues & solutions. Vehicular Communications, 20, 100182. [DOI:https://doi.org/10.1016/j.vehcom.2019.100182]
23. Verma, P., & Sood, S. (2017). Internet of Things-based student performance evaluation framework. Behaviour & Information Technology, 1-18. [DOI:10.1080/0144929X.2017.1407824]
24. Wang, T., Bhuiyan, M. Z. A., Wang, G., Rahman, M. A., Wu, J., & Cao, J. (2018). Big Data Reduction for a Smart City’s Critical Infrastructural Health Monitoring. IEEE Communications Magazine, 56(3), 128-133. [DOI:10.1109/MCOM.2018.1700303]
Send email to the article author
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
