Skip to main content
SpringerLink
Log in

Generative Adversarial Networks with Quantum Optimization Model for Mobile Edge Computing in IoT Big Data

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In present times, a massive quantity of big data has been generated by the Internet of Things (IoT) devices for a wide range of applications. The IoT devices generate an enormous data quantity that is troublesome for data processing and analytics functionalities, which is effortlessly managed by the cloud before the explosive development of the IoT. Specifically, the big IoT data analytics by mobile edge computing (MEC) becomes a hot research topic and needs comprehensive research works for intelligent decision making. This paper introduces a new generative adversarial network (GAN) with a quantum elephant herd optimization (QEHO) algorithm for MEC in IoT enabled big data environment called GAN-QEHO. The presented GAN-QEHO algorithm follows two-stage processes, namely feature selection (FS) and data classification. The QEHO algorithm is used to elect an optimal feature subset for the FS process. By the quantization of elephant individuals, the search scope of feature space can be enhanced, and an optimal tradeoff has been attained among exploration and exploitation. Then, the GAN model is employed for the classification process to identify different class labels. In order to validate the experimental results analysis of the GAN-QEHO algorithm, a series of simulations take place in terms of diverse aspects.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data availability

Data used in this study is available upon request to the corresponding author.

References

  1. Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of things (iot): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645–1660.

    Article  Google Scholar 

  2. Jararweh, Y., Doulat, A., AlQudah, O., Ahmed, E., Al-Ayyoub, M., & Benkhelifa, E. (2016). The future of mobile cloud computing: integrating cloudlets and mobile edge computing. In International Conference on Telecommunications, IEEE, pp. 1–5.

  3. Abbas, N., Zhang, Y., Taherkordi, A., & Skeie, T. (2018). Mobile edge computing: A survey. IEEE Internet of Things Journal, 5(1), 450–465.

    Article  Google Scholar 

  4. Snijders, C., Matzat, U., & Reips, U. D. (2012). “Big Data”: Big gaps of knowledge in the field of internet science[J]. International Journal of Internet Science, 7(1), 1–5.

    Google Scholar 

  5. Qureshi, N. M. F., Siddiqui, I. F., Unar, M. A., Uqaili, M. A., Nam, C. S., Shin, D. R., Kim, J. H., Abbas, A., & Bashir, A. K. (2018). An Aggregate Map Reduce Data Block Placement Strategy for Wireless IoT Edge Nodes in Smart Grid. Wireless Personal Communication. Springer.

    Google Scholar 

  6. Shafiq, M., Tian, Z., Bashir, A. K., Du, X., & Guizani, M. (2021). CorrAUC: a Malicious Bot-IoT Traffic Detection Method in IoT Network Using Machine Learning Techniques. IEEE Internet of Things Journal, 8(5), 3242–3254.

    Article  Google Scholar 

  7. Abbas, A., Siddiqui, I. F., Lee, S. U. J., Bashir, A. K., Ejaz, W., & Qureshi, N. M. F. (2018). Multi-Objective Optimum Solutions for IoT-based Feature Models of Software Product Line. IEEE Access., 6, 12228–12239.

    Article  Google Scholar 

  8. Dean, J., & Ghemawat, S. (2004). MapReduce: Simplified data processing on large clusters [J]. Operating Systems Design and Implementation (OSDI), 51(1), 107–113.

    Google Scholar 

  9. Hadoop. http://hadoop.apache.org/Introduction.

  10. Ceph Introduction. http://ceph.com/

  11. Rathore, M. M., Ahmad, A., & Paul, A. (2016). IoT-based smart city development using big data analytical approach. In Proceedings of IEEE International Conference on Automation (ICA-ACCA), pp. 1–8.

  12. Alam, F., Mehmood, R., Katib, I., & Albeshri, A. (2016). Analysis of eight data mining algorithms for smarter Internet of Things (IoT). Procedia Computer Science, 98, 437–442.

    Article  Google Scholar 

  13. Huang, C.-M., Shao, C.-H., Xu, S.-Z., & Zhou, H. (2017). The social Internet of Thing (S-IOT)-based mobile group handoff architecture and schemes for proximity service. IEEE Transaction on Emerging Topics in Computing, 5(3), 425–437.

    Article  Google Scholar 

  14. Jian, A., Gui, X., Zhang, W., & He, X. (2013). Social relation predictive model of mobile nodes in Internet of Things. Elektronika Elektrotechnika, 19(4), 81–86.

    Google Scholar 

  15. Rasooli, A., & Down, D. G. (2012). A hybrid scheduling approach for scalable heterogeneous hadoop systems. In Proceedings of SC Companion High Perform. Computer, Networking, Storage Anal. (SCC), pp. 1284–1291

  16. Hasan, M. Z., & Al-Turjman, F. SWARM-based data delivery in Social Internet of Things. In Future Generat. Comput. Syst., to be published.

  17. Ahmad, A., et al. (2017). Toward modeling and optimization of features selection in big data based social Internet of Things. Future Generation Computer Systems, 82, 715–726.

    Article  Google Scholar 

  18. Mardini, W., Khamayseh, Y., Yassein, M. B., & Khatatbeh, M. H. (2017). Mining Internet of Things for intelligent objects using genetic algorithm. Computers and Electrical Engineering, 66, 423–434.

    Article  Google Scholar 

  19. Baker, T., Aldawsari, B., Asim, M., Tawfik, H., Maamar, Z., & Buyya, R. (2018). Cloud-SEnergy: A bin-packing based multi-cloud service broker for energy efficient composition and execution of data-intensive applications. Sustainable Computing Informatics and systems, 19, 242–252.

    Article  Google Scholar 

  20. Vimal, S., Khari, M., Dey, N., Crespo, R. G., & Robinson, Y. H. (2020). Enhanced resource allocation in mobile edge computing using reinforcement learning based MOACO algorithm for IIOT. Computer Communications, 151, 355–364.

    Article  Google Scholar 

  21. Vimal, S., Khari, M., Crespo, R. G., Kalaivani, L., Dey, N., & Kaliappan, M. (2020). Energy enhancement using Multiobjective Ant colony optimization with Double Q learning algorithm for IoT based cognitive radio networks. Computer Communications, 154, 481–490.

    Article  Google Scholar 

  22. Vimal, S., Suresh, A., Subbulakshmi, P., Pradeepa, S. & Kaliappan, M. (2020). Edge computing-based intrusion detection system for smart cities development using IoT in urban areas. In Internet of things in smart Technologies for Sustainable Urban Development (pp. 219–237). Springer.

  23. Fiore, U., De Santis, A., Perla, F., Zanetti, P., & Palmieri, F. (2019). Using generative adversarial networks for improving classification effectiveness in credit card fraud detection. Information Sciences, 479, 448–455.

    Article  Google Scholar 

  24. Li, J., Guo, L., Li, Y., & Liu, C. (2019). Enhancing elephant herding optimization with novel individual updating strategies for large-scale optimization problems. Mathematics, 7(5), 395.

    Article  Google Scholar 

  25. Wang, D., Chen, H., Li, T., Wan, J., & Huang, Y. (2020). A novel quantum grasshopper optimization algorithm for feature selection. International Journal of Approximate Reasoning, 127, 33–53.

    Article  MathSciNet  Google Scholar 

  26. Wang, G. G., Deb, S., & Coelho, L. D. S. (2015). Elephant herding optimization. In Proceedings of the 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI 2015), Bali, Indonesia, 7–9 December 2015, pp. 1–5.

Download references

Funding

The authors did not receive support from any organization for the submitted work.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Ajay Kumar Garg Engineering College, Ghaziabad, Uttar Pradesh, India

    Inderjeet Kaur

  2. Computer Science and Engineering, Vignan’s Institute of Information Technology (Autonomous), Visakhapatnam, India

    E. Laxmi Lydia

  3. Department of Computer Science and Engineering, South Point Group of Institutions, Sonepat, Haryana, India

    Vinay Kumar Nassa

  4. Department of Electronic Engineering, Kwangwoon University, Seoul, 01897, Korea

    Bhanu Shrestha

  5. College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, 11942, Saudi Arabia

    Jamel Nebhen

  6. Department of Information Technology, Faculty of Computing and Information Technology in Rabigh, King Abdulaziz University, Jeddah, 21911, Saudi Arabia

    Sharaf Malebary

  7. Department of Computer Science and Engineering, Sejong University, Seoul, 05006, Korea

    Gyanendra Prasad Joshi

Authors
  1. Inderjeet Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. E. Laxmi Lydia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vinay Kumar Nassa
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bhanu Shrestha
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jamel Nebhen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sharaf Malebary
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Gyanendra Prasad Joshi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization, IK, GPJ, ELL; Data curation, ELL, VKN; Formal analysis, BS, ELL; Funding acquisition, JN, SM, GPJ; Investigation, IK, GPJ; Methodology, IK, BS; Project administration, VKN; Resources, JN, SM, GPJ; Software, BS, JN, SM; Supervision, GPJ, ELL; Validation, CS; Visualization, GPJ; Writing—original draft, IK; Writing—review & editing, GPJ.

Corresponding author

Correspondence to Gyanendra Prasad Joshi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest. The manuscript was written through the contributions of all authors. All authors have given approval to the final version of the manuscript.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kaur, I., Lydia, E.L., Nassa, V.K. et al. Generative Adversarial Networks with Quantum Optimization Model for Mobile Edge Computing in IoT Big Data. Wireless Pers Commun 127, 1565–1585 (2022). https://doi.org/10.1007/s11277-021-08706-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-08706-7

Keywords

Search

Navigation