A Link Quality Estimation Method for Wireless Sensor Networks Based on Deep Forest

He, Mu; Shu, Jian

doi:10.1109/access.2020.3047648

Cited by 7 publications

(1 citation statement)

References 22 publications

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“…Estimating wireless link quality is a central problem in the field of wireless networks, including IoTs and WMNs [19] - [20], while its importance is also highlighted for SDN-based wireless networks [21] - [22]. A vast amount of algorithms have been proposed for link quality detection, spanning from statistical based procedures to machine learning (ML) approaches and deep-learning procedures.…”

Section: Related Workmentioning

confidence: 99%

A Link-Quality Anomaly Detection Framework for Software-Defined Wireless Mesh Networks

Skaperas,

Mamatas,

Tsaoussidis

2024

Trans. Mach. Learn. Comm. Netw.

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Software-defined wireless mesh networks are being increasingly deployed in diverse settings, such as smart cities and public Wi-Fi access infrastructures. The signal propagation and interference issues that typically characterize these environments can be handled by employing SDN controller mechanisms, effectively monitoring link quality and triggering appropriate mitigation strategies, such as adjusting link and/or routing protocols. In this paper, we propose an unsupervised machine learning (ML) online -for link quality detection consisting of: (i) an improved preprocessing clustering algorithm, based on elastic similarity measures, to efficiently characterize wireless links in terms of reliability, and, (ii) a novel change point (CP) detector for the real-time identification of anomalies in the quality of selected links, which minimizes the overestimation error through the incorporation of a rank-based test and a recursive max-type procedure. In this sense, considering the communication constraints of such environments, our approach minimizes the detection overhead and the inaccurate decisions caused by overestimation. The proposed detector is validated, both on its individual components and as an overall mechanism, against synthetic but also real data traces; the latter being extracted from real wireless mesh network deployments.

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