Smart Steering with Machine Learning for Wireless Mesh Networks

In wireless networks, clients can be steered from one access point (AP) to another for a better internet connection. Although this client steering has large potential to improve overall network service and the user experience, such steering actions may not always yield the desired result and the client may remain persistently connected to its current AP. This issue is referred to as the sticky client problem, which prevents the intended improvement in the network. In this work, in order to address the sticky client problem, Support Vector Machine (SVM) as a batch method and kernel perceptron as an online method are examined based on various network features. Nonlinear classifiers of correct steering actions have been trained to maximize the accuracy of steering actions. In particular, the online kernel perceptron performs sequential learning at APs using the cloud data to decide about steering actions in real time. This algorithm is data-driven, and able to provide optimum steering in realtime. In our experiments, we observed that our batch approach identifies successful steering actions with %95 accuracy. On the other hand, our online algorithm is able to approximate the batch performance by a small margin while allowing real time steering with significantly lower computational complexity.