Abstract
The fourth industrial revolution is the current trend of automation. Devices that are connected with multiple networks are still unable to connect fastly, and the world is looking for the application of the massive lot, whereas the 5G network is enabled to meet the demand. It is a dire need to automate the aviation industry with this technology. So, in this research, a Fuzzy Logic Controller (FLC) for the parking of aircrafts is proposed and all the calculations are done using the fuzzy logic controller toolbox of MATLAB. An aircraft can be parked without human interference by seeking the information of flights and the availability of parking space in the garage along with the clearance of the runway. Membership values of FLC are taken in the form of a neutrosophic number and then converted into a fuzzy number using the accuracy function. Results evaluate that 5G will automate the Aviation automation industry with precision and efficiency in operation. 5G also has an ability of flexibility support to the upcoming technologies which are unknown to us.
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References
Le, L.B., Lau, V., Jorswieck, E., Dao, N.D., Haghighat, A., Kim, D.I., Le-Ngoc, T.: Enabling 5G mobile wireless technologies. J. Wireless Com. Network 218 (2015) https://doi.org/10.1186/s13638-015-0452-9
Agyapong, P.K., Iwamura, M., Staehle, D., Kiess, W., Benjebbour, A.: Design considerations for a 5G network architecture. IEEE Commun. Mag. 52(11), 65–75 (2014)
Bergren, S. (2017). Design Considerations for a 5G Network Architecture. arXiv preprint arXiv:1705.02902
Thompson, J., Ge, X., Wu, H.C., Irmer, R., Jiang, H., Fettweis, G., Alamouti, S.: 5G wireless communication systems: prospects and challenges [Guest Editorial]. IEEE Commun. Mag. 52(2), 62–64 (2014)
Hu, Y.C., Patel, M., Sabella, D., Sprecher, N., Young, V.: Mobile edge computing—a key technology towards 5G. ETSI White Paper 11(11), 1–16 (2015)
Manasa, H.R., Pramila, S.: Architecture and technology components for 5G mobile and wireless communication. Int. J. Eng. Res. 4(06) (2015)
CNBC Newsletters.: (2019) https://www.cnbc.com/2019/11/25/5g-will-span-two-thirds-of-global-population-in-6-years-ericsson-says.html
Atalık, O., Akan, Ş., Bakır, M.: Aviation 4.0: current practises of industry 4.0 in the airline and airport industries. (2019) https://doi.org/10.6084/m9.figshare.10316303
Kivits, R., Charles, M.B., Ryan, N.: A post-carbon aviation future: airports and the transition to a cleaner aviation sector. Futures 42, 199–211 (2010)
Lee, J., Mo, J.: Analysis of technological innovation and environmental performance improvement in aviation sector. Int. J. Environ. Res. Public Health 8, 3777–3795 (2011). https://doi.org/10.3390/ijerph8093777
Havle, C.A., Üçler, Ç.: Enablers for industry 4.0. In: 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), pp. 1–6. IEEE (2018)
Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M.: Industry 4.0: The Future of Productivity and Growth in Manufacturing Industries, pp. 1–14. (2015) https://www.zvw.de/media.media.72e472fb-1698-4a15-8858-344351c8902f.original.pdf
Valdés, R., Gomez, C., Victor, F., Sanz, Á., Javier, P.C.: Aviation 4.0: more safety through automation and digitization. pp. 25–41. (2018). https://doi.org/10.5772/intechopen.73688
Ustundag, A., Cevikcan, E.: Industry 4.0: managing the digital transformation. Springer, Cham, (2017)
Albreem, M.A.: 5G wireless communication systems: vision and challenges. In: 2015 International Conference on Computer, Communications, and Control Technology (I4CT). pp. 493–497. IEEE. (April 2015)
Idowu-Bismark, O., Okokpujie, K.O., Ryan, H., Adedokun, M.O.: 5G wireless communication network architecture and its key enabling technologies. Int. Rev. Aerosp. Eng. (I. RE. AS. E) 12(2), 70–82 (2019)
Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)
Atanassov, K.T.: Intuitionistic fuzzy sets. Fuzzy Sets Syst. 20(1), 87–96 (1986)
Smarandache, F.: Neutrosophy. Neutrosophic probability, set, and logic. ProQuest Information and Learning, Ann Arbor, Michigan, USA (1998)
Molodtsov, D.: Soft set theory-first results. Comput. Math. Appl. 37, 19–31 (1999)
Maji, P., Biswas, R., Roy, A.R.: Fuzzy soft sets. Fuzzy Sets Syst. 9, 589–602 (2001a)
Maji, P., Biswas, R., Roy, A.: Intuitionistic fuzzy soft sets. J. Fuzzy Math. 9 (2001b)
Maji, P.K.: Neutrosophic soft set. Ann. Fuzzy Math. Inform. 5(1), 157–168 (2013)
Abdel-Baset, M., Chang, V., Gamal, A.: Evaluation of the green supply chain management practices: a novel neutrosophic approach. Comput. Ind. 108, 210–220 (2019)
Riaz, M., Naeem, K., Ahmad, M.O.: Novel concepts of soft sets with applications. Ann. Fuzzy Mat. Inform. 13(2), 239–251 (2017)
Wielki, J., Jurczyk, M., Madera, D.: Application of TOPSIS method for evaluation of IT application in the hospital. (2019). https://doi.org/10.34190/KM.19.134
Calvo-Flores, M., Verdegay, J., Vila, M.: Linguistic decision-making models. Int. J. Intell. Syst. 7, 479–492 (1992). https://doi.org/10.1002/int.4550070507
Liu, J.: Fuzzy logic control. (2018) https://doi.org/10.1007/978-981-10-5263-7_4
Mamdani, E.H.: Application of fuzzy algorithms for control of simple dynamic plant. Proc. Inst. Electr. Eng. 121(12), 1585–1588 (1974)
Shen, J., Jin, D., Li, Z.: Fuzzy logic control and fuzzy logic control chip. 25, 61–66, 60 (1997)
Suwoyo, H., Tian, Y.Z., Hajar, M.: An flc-pso algorithm-controlled mobile robot. SINERGI. 24, 177 (2020). https://doi.org/10.22441/sinergi.2020.3.002
Jafar, M.N., Saqlain, M., Mansoob, A., Riffat, A.: A best way to access gas station using fuzzy logic controller in neutrosophic environment. Sci. Inq. Rev. 4(1), 30–45 (2020). Available at: https://doi.org/10.32350/sir.41.03
Saqlain, M., Saeed, M., Saeed, H.M.: Smart parking system using fuzzy logic controller for alien cities. Int. J. Math. Res. 9(1), 62–71 (2020). https://doi.org/10.18488/journal.24.2020.91.62.71
Chatterjee, K., Kar, M.B., Kar, S.: Strategic decisions using intuitionistic fuzzy VIKOR method for information system (IS) outsourcing. In: 2013 International Symposium on Computational and Business Intelligence (ISCBI), pp. 123–126. IEEE, (August 2013)
Havle, C.A., Kılıç, B.: A hybrid approach based on the fuzzy AHP and HFACS framework for identifying and analyzing gross navigation errors during transatlantic flights. J. Air Transp. Manag. 76, 21–30 (2019)
Şenel, M., Şenel, B., Havle, C.A.: Risk analysis of ports in maritime industry in Turkey using FMEA based intuitionistic fuzzy TOPSIS approach. In: ITM Web of Conferences, vol. 22, p 01018. EDP Sciences (2018)
Franco Barbosa G.: Aviation manufacturing towards to industry 4.0: a review. In: 4th International Conference and Exhibition on Mechanical & Aerospace Engineering, Florida, (2016)
Troiano, A., Pasero, E.: A runway surface monitor using internet of things. J. Electr. Eng.-Elektrotechnicky Casopis 65(3), 169–173 (2014)
Hwang, C., Yoon, K.: Multiple Attribute Decision Making: Methods and Applications. Springer, A State-of-the-Art Survey (1981)
Dempsey, J.S.: Introduction to Private Security. Thomson Wadsworth, Belmont, CA, p. 78 (2008)
Ross, T.J.: Fuzzy Logic with Engineering Applications, pp. 90–91. Wiley India, (2010)
Van Leekwijck, W., Kerre, E.E.: Defuzzification: criteria and classification. Fuzzy Sets Syst. 108(2), 159–178 (1999)
Ben-Ari, M., Mondada, F. (2018). Fuzzy logic control. https://doi.org/10.1007/978-3-319-62533-1_11
Büyüközkan, G., Feyzioglu, O., Havle, C. (2020). Analysis of Success Factors in Aviation 4.0 Using Integrated Intuitionistic Fuzzy MCDM Methods. https://doi.org/10.1007/978-3-030-23756-1_73
Global Mobile Supplier Association.: (2019). https://gsacom.com/paper/lte-5g-market-statistics-8-april-2019/
Gupta, A., Jha, R.K.: A survey of 5G network: architecture and emerging technologies. IEEE Access 3, 1206–1232 (2015)
Notlagh, N.H., Taleb, T., Arouk, O.: Low-altitude unmanned aerial vehicles-based internet of things services: comprehensive survey and future perspectives. IEEE Int. Things J. 3(6), 889–922 (2016)
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Saqlain, M., Saeed, M. (2022). Fuzzy Logic Controller for Aviation Parking with 5G Communication Technology. In: Kahraman, C., Aydın, S. (eds) Intelligent and Fuzzy Techniques in Aviation 4.0. Studies in Systems, Decision and Control, vol 372. Springer, Cham. https://doi.org/10.1007/978-3-030-75067-1_3
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