An Overview of Machine Learning Approaches in Wireless Mesh Networks

TL;DR

This paper reviews how machine learning techniques are applied to optimize wireless mesh networks, addressing challenges in capacity, QoS, security, and fault tolerance, and suggests future research directions.

Contribution

It provides a comprehensive overview of ML applications in WMNs, analyzing past efforts, identifying issues, and proposing potential solutions and future research directions.

Findings

ML techniques have been successfully applied to WMN optimization

Existing issues include security and fault tolerance challenges

Future research can enhance ML integration for better WMN performance

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, 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.