International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
권호 표지
DOI QR코드

DOI QR Code

An Intelligent Machine Learning Inspired Optimization Algorithm to Enhance Secured Data Transmission in IoT Cloud Ecosystem

  • Received : 2022.06.05
  • Published : 2022.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

Traditional Cloud Computing would be unable to safely host IoT data due to its high latency as the number of IoT sensors and physical devices accommodated on the Internet grows by the day. Because of the difficulty of processing all IoT large data on Cloud facilities, there hasn't been enough research done on automating the security of all components in the IoT-Cloud ecosystem that deal with big data and real-time jobs. It's difficult, for example, to build an automatic, secure data transfer from the IoT layer to the cloud layer, which incorporates a large number of scattered devices. Addressing this issue this article presents an intelligent algorithm that deals with enhancing security aspects in IoT cloud ecosystem using butterfly optimization algorithm.

Keywords

References

  1. Sniderman, B.; Mahto, M.; Cotteleer, M.J. Industry 4.0 and Manufacturing Ecosystems; Deloitte University Press: London, UK, 2016; pp. 1-23.
  2. Corotinschi, G.; Gaitan, V.G. Enabling IoT connectivity for Modbus networks by using IoT edge gateways. In Proceedings of the 2018 International Conference on Development and Application Systems (DAS), Suceava, Romania, 24-26 May 2018; pp. 175-179.
  3. Geissbauer, R.; Schrauf, S.K.V. Industry 4.0-Opportunities and Challanges of the Industrial Internet. Available online: https://www.strategyand.pwc.com/gx/en/insights/2015/industrial-internet.html (accessed on 2 February 2021).
  4. Franko, A.; Vida, G.; Varga, P. Reliable Identification Schemes for Asset and Production Tracking in Industry 4.0. Sensors 2020, 20, 3709. https://doi.org/10.3390/s20133709
  5. Massaro, A.; Galiano, A. Re-engineering process in a food factory: An overview of technologies and approaches for the design of pasta production processes. Prod. Manuf. Res. 2020, 8, 80-100.
  6. Weerasiri, D.; Barukh, M.C.; Benatallah, B.; Sheng, Q.Z.; Ranjan, R. A Taxonomy and Survey of Cloud Resource Orchestration Techniques. ACM Comput. Surv. 2017, 50, 1-41. https://doi.org/10.1145/3054177
  7. Maiti, P.; Shukla, J.; Sahoo, B.; Turuk, A.K. QoS-aware fog nodes placement. In Proceedings of the 2018 4th International Conference on Recent Advances in Information Technology (RAIT), Dhanbad, India, 15-17 March 2018; pp. 1-6.
  8. Groover, M. Fundamentals of Modern Manufacturing: Materials, Processes, and Systems; John Wiley & Sons, Inc: Hoboken, NJ, USA, 2020.
  9. Deshmukh, U.; More, S.A. Fog Computing: New Approach in the World of Cloud Computing. FInt. J. Innov. Res. Comput.Commun. Eng. 2016, 4, 16310-16316.
  10. Luan, T.H.; Gao, L.; Li, Z.; Xiang, Y.; Wei, G.; Sun, L. Fog computing: Focusing on mobile users at the edge. arXiv 2015, arXiv:1502.01815
  11. Puliafito, C.; Vallati, C.; Mingozzi, E.; Merlino, G.; Longo, F.; Puliafito, A. Container Migration in the Fog: A Performance Evaluation. Sensors 2019, 19, 1488. https://doi.org/10.3390/s19071488
  12. Gil, D.; Ferrandez, A.; Mora-Mora, H.; Peral, J. Internet of things: A review of surveys based on context aware intelligent services. Sensors 2016, 16, 1069. https://doi.org/10.3390/s16071069
  13. Perera, C.; Qin, Y.; Estrella, J.C.; Reiff-Marganiec, S.; Vasilakos, A.V. Fog Computing for Sustainable Smart Cities. ACM Comput. Surv. 2017, 50, 1-44. https://doi.org/10.1145/3057266
  14. Naha, R.K.; Garg, S.; Georgakopoulos, D.; Jayaraman, P.P.; Gao, L.; Xiang, Y.; Ranjan, R. Fog Computing: Survey of Trends, Architectures, Requirements, and Research Directions. IEEE Access 2018, 4, 1-31.
  15. Maag, B.; Zhou, Z.; Thiele, L. A survey on sensor calibration in air pollution monitoring deployments. IEEE Internet Things J. 2018, 5, 1-15. https://doi.org/10.1109/JIOT.2017.2773600
  16. Mukherjee, M.; Shu, L.; Wang, D. Survey of fog computing: Fundamental, network applications, and research challenges. IEEE Commun. Surv. Tutor. 2018, 20, 1-30. https://doi.org/10.1109/COMST.2018.2814571
  17. Al-Fuqaha, A.; Guizani, M.; Mohammadi, M.; Aledhari, M.; Ayyash, M. Internet of things: A survey on enabling technologies, protocols, and applications. IEEE Commun. Surv. Tutor. 2015, 17, 2347-2376. https://doi.org/10.1109/COMST.2015.2444095
  18. Yassein, M.B.; Shatnawi, M.Q.; Aljwarneh, S.; Al-Hatmi, R. Internet of Things: Survey and open issues of MQTT protocol. In Proceedings of the 2017 International Conference on Engineering & MIS (ICEMIS), Monastir, Tunisia, 8-10 May 2017.
  19. Maheswari, K.; Bhanu, S.S.; Nickolas, S. A Survey on Data Integrity Checking and Enhancing Security for Cloud to FogComputing. In Proceedings of the IEEE Xplore, Bangalore, India, 5-7 March 2020; pp. 121-127.