Spectral Graph Theory-Based Knowledge Representation for Analyzing Wireless Mesh Networks

Abstract

The analysis of Wireless Mesh Networks (WMNs) has traditionally focused on key performance metrics such as throughput and connectivity. However, this paper introduces a novel and advanced approach that utilizes spectral graph theory-based knowledge representation to more effectively analyze the topological and functional characteristics of WMNs. By carefully examining the eigenvalues and eigenvectors of the network’s graph Laplacian, this method uncovers deep underlying patterns in connectivity, resilience, and overall efficiency. These insights provide a more comprehensive understanding of network performance and reliability, moving beyond traditional evaluation techniques. The method offers a systematic and effective way to visualize and optimize the structures of WMNs, leading to the potential for more efficient network design. Ultimately, this approach contributes valuable knowledge that can enhance both the theoretical and practical understanding of WMNs, offering new avenues for improving network performance and resilience.

Received: 30 December 2022 | Revised: 17 January 2023 | Accepted: 22 February 2023

Conflicts of Interest

The author declares that he has no conflicts of interest to this work.

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Author Contribution Statement

Nenad M. Jovanovic: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition.