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

2026-03-23

AI Summary

This work addresses the challenge of achieving both high prediction accuracy and scalability in resource-constrained centralized Wi-Fi controllers, where a single global model struggles to meet the demands of large-scale networks. The authors propose a cluster-oriented, customized modeling paradigm that first groups Wi-Fi time-series data through feature engineering and principal component analysis (PCA), then constructs dedicated prediction models for each cluster. This approach significantly reduces the mean absolute error (MAE) while effectively balancing predictive accuracy against system resource consumption. By enabling selective deployment and adaptive network management, the method achieves a synergistic optimization of scalability and precision, making it well-suited for dynamic, large-scale Wi-Fi environments.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Machine Learning: Scalability of ML Systems
• Cognitive Modeling & Cognitive Systems: Applications

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• predictive modeling
• resource-constrained
• Wi-Fi networks
• scalability
• centralized control

Innovation

• cluster-specific modeling
• predictive modeling
• feature-based clustering
• resource-constrained systems
• Wi-Fi network management