  Wireless Mesh Networks (WMNs) have been extensively studied for nearly two
decades as one of the most promising candidates expected to power the high
bandwidth, high coverage wireless networks of the future. However, consumer
demand for such networks has only recently caught up, rendering efforts at
optimizing WMNs to support high capacities and offer high QoS, while being
secure and fault tolerant, more important than ever. To this end, a recent
trend has been the application of Machine Learning (ML) to solve various design
and management tasks related to WMNs. In this work, we discuss key ML
techniques and analyze how past efforts have applied them in WMNs, while noting
some existing issues and suggesting potential solutions. We also provide
directions on how ML could advance future research and examine recent
developments in the field.
