A Machine Learning Based Forwarding Algorithm Over Cognitive Radios in Wireless Mesh Networks

TL;DR

This paper introduces a machine learning-based forwarding algorithm for wireless mesh networks that dynamically selects the best next hop based on predicted bandwidth, improving transmission speed and efficiency.

Contribution

It presents a novel machine learning approach that predicts link bandwidth using minimal past data, combined with a geometrical algorithm to optimize forwarding region selection.

Findings

Significantly faster transmission compared to peer algorithms

Effective bandwidth prediction with minimal historical data

Improved network throughput and reduced flooding

Abstract

Wireless Mesh Networks improve their capacities by equipping mesh nodes with multi-radios tuned to non-overlapping channels. Hence the data forwarding between two nodes has multiple selections of links and the bandwidth between the pair of nodes varies dynamically. Under this condition, a mesh node adopts machine learning mechanisms to choose the possible best next hop which has maximum bandwidth when it intends to forward data. In this paper, we present a machine learning based forwarding algorithm to let a forwarding node dynamically select the next hop with highest potential bandwidth capacity to resume communication based on learning algorithm. Key to this strategy is that a node only maintains three past status, and then it is able to learn and predict the potential bandwidth capacities of its links. Then, the node selects the next hop with potential maximal link bandwidth. Moreover, a geometrical based algorithm is developed to let the source node figure out the forwarding region in order to avoid flooding. Simulations demonstrate that our approach significantly speeds up the transmission and outperforms other peer algorithms.