Cluster-Specific Predictive Modeling: A Scalable Solution for Resource-Constrained Wi-Fi Controllers

TL;DR

This paper introduces a scalable approach for resource-constrained Wi-Fi controllers by using clustering and cluster-specific predictive models to improve forecasting accuracy in large networks.

Contribution

It presents a novel integration of clustering algorithms with predictive modeling to enhance scalability and accuracy in Wi-Fi network management under resource constraints.

Findings

Cluster-specific models outperform global models in high-activity clusters.

Feature-based clustering improves model accuracy and scalability.

Trade-offs between model complexity and resource utilization are analyzed.

Abstract

This manuscript presents a comprehensive analysis of predictive modeling optimization in managed Wi-Fi networks through the integration of clustering algorithms and model evaluation techniques. The study addresses the challenges of deploying forecasting algorithms in large-scale environments managed by a central controller constrained by memory and computational resources. Feature-based clustering, supported by Principal Component Analysis (PCA) and advanced feature engineering, is employed to group time series data based on shared characteristics, enabling the development of cluster-specific predictive models. Comparative evaluations between global models (GMs) and cluster-specific models demonstrate that cluster-specific models consistently achieve superior accuracy in terms of Mean Absolute Error (MAE) values in high-activity clusters. The trade-offs between model complexity (and accuracy) and resource utilization are analyzed, highlighting the scalability of tailored modeling approaches. The findings advocate for adaptive network management strategies that optimize resource allocation through selective model deployment, enhance predictive accuracy, and ensure scalable operations in large-scale, centrally managed Wi-Fi environments.