Dynamic bandwidth allocation with machine learning in dense WiFi network

Abstract

Efficient load balancing is a fundamental aspect within a densely populated Wireless Fidelity (WiFi) network, particularly when the goal is to evenly distribute bandwidth and regulate Internet usage. The distribution of network traffic for load balancing can be achieved by utilizing quality of service protocols and access control lists or filters. This document introduces the application of a machine learning-based prediction model to outline time intervals of congestion in a densely populated WiFi network employing dynamic load balancing. Historical data related to associated users and WiFi network bandwidth serve as key indicators to assess the network's congestion level. Dynamic load balancing is executed by allocating bandwidth to priority traffic based on observed congestion levels. To implement load balancing, access control lists based on time and quality of service parameters are configured within the network devices.The experimental findings reveal a notable improvement in the performance of a WiFi network through the implementation of our dynamic load balancing approach, resulting in a 50% reduction in lost packets. This innovative method allows for the definition of priority traffic types during different congestion periods.