International Journal on Advanced Science, Engineering and Information Technology, Vol. 11 (2021) No. 4, pages: 1487-1493, DOI:10.18517/ijaseit.11.4.14463

Design a Model-Based on Nonlinear Multiple Regression to Predict the Level of User Satisfaction when Optimizing a Traditional WLAN Using SDWN

Leonel Hernandez, Carlos Eduardo Uc Rios, Andri Pranolo

Abstract

Higher education institutions' wireless networks have different roles and network requirements, ranging from educational platforms and informative consultations. Currently, the inefficient use of network resources, poor wireless planning, and other factors, affect having a robust and stable network platform. Different authors have investigated the various strategies for the optimization of wireless infrastructures. Still, most of the cases studied aim to improve traditional performance variables without considering maximizing the level of user satisfaction, which represents a flaw that this research paper hopes to solve through SDWN and a predictive model.  The authors will determine an appropriate methodology to estimate the user's level of satisfaction through an algorithm or predictive model based on nonlinear multiple regression supported on network performance variables, making a characterization of the project's environment analyzing the wireless conditions. The investigation phases will follow the life cycle guidelines defined by the Cisco PPDIOO methodology (Prepare, Plan, Design, Implement, Operate, Optimize). As a result, it is expected that the project will be the beginning of academic research that will help create strategies to optimize the WiFi network of any educational institution to maximize user satisfaction. In short, the optimization process provides the network with differentiating factors through a modular design with variable modification of parameters according to the users' requirements and needs.

Keywords:

Software-Defined Wireless Networks (SDWN); optimization; predictive model; wireless networks; PPDIOO.

Viewed: 81 times (since abstract online)

cite this paper     download