Abstract
In this paper we focus on knowledge extraction from large-scale wireless networks through stream processing. We present the primary methods for sampling, data collection, and monitoring of wireless networks and we characterize knowledge extraction as a machine learning problem on big data stream processing. We show the main trends in big data stream processing frameworks. Additionally, we explore the data preprocessing, feature engineering, and the machine learning algorithms applied to the scenario of wireless network analytics. We address challenges and present research projects in wireless network monitoring and stream processing. Finally, future perspectives, such as deep learning and reinforcement learning in stream processing, are anticipated.
Highlights
The popularization of smartphones and Internet of Things IoT devices has driven the growth of mobile data generation via wireless networks [1]
8 Conclusion Wireless access networks are growing in number, variety, and speed
Several studies show that data from these networks are of critical value to network monitoring, management, and control
Summary
The popularization of smartphones and Internet of Things IoT devices has driven the growth of mobile data generation via wireless networks [1]. The analysis of a large volume of data from large-scale wireless networks enables identifying usage patterns, defining user profiles, detecting failures or performance drops at specific sites in the network, and optimizing channel allocation This analysis is challenging due to the inherent characteristics of the wireless environment, such as user mobility, noise, and redundancy of the collected data. The purpose of this article is to survey the main real-time streaming data processing algorithms and techniques for extracting knowledge from large-scale wireless network monitoring, the so-called Wi-Fi Analytics. The contributions of this article are the following: (i) present an insightful overview of wireless network monitoring; (ii) provide a summary of big data processing techniques and tools; (iii) review a theoretical overview of streaming data processing and the use of real-time training machine learning algorithms; and (iv) present and discuss applications in the context of Wi-Fi Analytics. One approach to this characterization of the vicinity of an access point is to
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