Deep Learning Algorithm Using Densenet to Enable Big Data Analytics in Large WiFi Systems

Authors

  • Doradla Bharadwaja Assistant Professor, Department of Information Technology, Prasad V. Potluri Siddhartha Institute of Technology, Andhra Pradesh, India.
  • R. Gayathri Professor, Department of ECE, Rajalakshmi Engineering College, Tamil Nadu, India.
  • D. Sugumar Associate Professor, Department of ECE, Karunya Institute of Technology and Sciences (Deemed to be University), Tamil Nadu, India.
  • D. R. Denslin Brabin Professsor, Department of Computer Science and Engineering, DMI College of Engineering, Chennai.
  • Faiz Akram Assistant Professor, Faculty of Computing and Informatics, Jimma Institute of Technology, Jimma University, Ethiopia
  • Mohd. Javeed Mehdi Assistant Professor, Department of ECE, Gokaraju Rangaraju Institute of Engineering and Technology, Telangana, India.

Keywords:

Deep Learning, Densenets, Big Data Analytics, Wifi Systems

Abstract

The increasing mobile device and unceasing traffic demand enables the deployment of large-scale WiFi systems that offers indoor coverage and high-speed connectivity. The large-scale deployment of WiFi system is an on-going research in wireless system due to its challenging heterogeneous nature of access points. Such access points undergo rapid challenges due to traffic conditions and traffic consumptions with rapidly increasing input data. On other hand, massive connection with heavy traffic laden from the WiFi devices poses increased pressure on backhaul network and reduces the Quality of Service by the users. We have developed using DenseNets that reduces the backhaul traffic due to the WiFi access points. The study explores wide deployment of data cache from massive access points for serving the several thousand active users. The study reduces the backhaul traffic using deep learning model that conducts statistical analysis on the collected user records. Extensive simulations are conducted to study the efficacy of the model that includes the cumulative distribution function per access point traffic/entropy and Jaccard similarity, caching resource utility and cache gain ratio.

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References

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Published

05.12.2023

How to Cite

Bharadwaja, D. ., Gayathri, R. ., Sugumar, D. ., Brabin, D. R. D. ., Akram, F. ., & Mehdi, M. J. . (2023). Deep Learning Algorithm Using Densenet to Enable Big Data Analytics in Large WiFi Systems. International Journal of Intelligent Systems and Applications in Engineering, 12(7s), 326–331. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/4076

Issue

Vol. 12 No. 7s (2024)

Section

Research Article

License

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