LAUSR

Abstract Steering actions in wireless mesh networks refer to requesting clients to change their access points (AP) for better exploiting the mesh network and achieving higher quality connections. However, steering actions for especially the sticky clients do not always successfully produce the intended outcome. In this work, we address this issue from a machine learning perspective as we formulate a classification problem in both batch (SVM) and online (kernel perceptron) setting based on various network features. We train classifiers to learn the nonlinear regions of correct decisions to maximize the overall success probability in steering actions. In particular, the presented online kernel perceptron classifier (1) performs learning sequentially at the cloud from the entire data of multiple mesh networks and (2) operates at APs for steering; both are executed in real-time. The presented algorithm is completely data driven, adaptive, optimal in its steering and real-time, hence named as Online Machine Learning for Smart Steering. Our batch algorithm is observed in our experiments to achieve -at least- 95% of classification accuracy in identifying the conditions for successful steering. Our online algorithm -on the other hand- successfully approximates the baseline accuracy by a small margin with relatively negligible space and computational complexity, allowing real-time steering.