Wireless Mesh Networks Routing using Machine Learning

Wireless mesh networks (WMNs) are increasingly deployed in large-scale infrastructures, requiring efficient and reliable routing to ensure high performance. Traditional routing protocols such as RPL rely on local decision-making, which often leads to suboptimal global routing structures, particularly in dense or complex topologies. We investigated whether machine learning (ML) and deep learning (DL) techniques can be used to globally optimize Directed Acyclic Graph (DAG)-based routing in WMNs.