An Overview of Machine Learning Approaches in Wireless Mesh Networks

Abstract

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, key ML techniques are discussed and past efforts applying them in WMNs are analyzed, while noting some existing issues and suggesting potential solutions. Directions are provided on how ML could advance future research. Recent developments in the field are also examined.