Stickyless: An Intelligent Method for Solving Sticky Client Problem in Wi-Fi Networks

In IEEE 802.11-based access networks (Wi-Fi), the client remains connected to a far-poor Access Point (AP) rather than switching to a near-better AP. This scenario is termed a sticky client problem. This scenario can severely impact the performance of real-time applications. Several standards, such as 802.11k1v/r, are being developed to enhance Wi-Fi roaming capabilities. However, the sticky client problem is yet to be solved completely. This paper proposes Sticky less, a novel method that leverages machine learning to learn the home Wi-Fi network behavior to address the sticky client problem. Initially, Stickyless divides the deployment area of APs into distinct zones, generates training data, and subsequently trains the machine learning module. The Stickyless employs clustering models to recommend selecting the optimal AP within a specific zone by considering the application performance and quality metrics. To conclude, Stickyless assesses performance using a proposed scoring and cascading module. We also developed a prototype to evaluate the Stickyless performance, outperforming the existing methods. The proposed method improves the Wi-Fi roaming experience by reducing stickiness up to 40 %. Thereby, it improves the link quality of the client by an average of 19 % and decreases the packet error rate by up to 3.5 % compared to the existing approaches. We also experimented with popular gaming apps, and Stickyless reduced the latency by up to 7 -fold.