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Sensors 2016, 16(6), 790; doi:10.3390/s16060790

Data-Driven Design of Intelligent Wireless Networks: An Overview and Tutorial

Department of Information Technology, Ghent University-iMinds, Technologiepark-Zwijnaarde 15, Gent 9052, Belgium
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Author to whom correspondence should be addressed.
Academic Editor: Paolo Bellavista
Received: 29 March 2016 / Revised: 10 May 2016 / Accepted: 23 May 2016 / Published: 1 June 2016
(This article belongs to the Special Issue Intelligent Internet of Things (IoT) Networks)
View Full-Text   |   Download PDF [1709 KB, uploaded 1 June 2016]   |  

Abstract

Data science or “data-driven research” is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves. View Full-Text
Keywords: wireless networks; data science; data-driven research; machine learning; knowledge discovery; cognitive networking; intelligent systems wireless networks; data science; data-driven research; machine learning; knowledge discovery; cognitive networking; intelligent systems
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary material

  • Externally hosted supplementary file 1
    Link: https://github.com/merimak/DataDrivenDesignWirelessNetworks
    Description: This link points to the repository that contains a set of scripts and results that were used in the tutorial paper for solving a specific wireless networking problem using a data-driven approach.

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MDPI and ACS Style

Kulin, M.; Fortuna, C.; De Poorter, E.; Deschrijver, D.; Moerman, I. Data-Driven Design of Intelligent Wireless Networks: An Overview and Tutorial. Sensors 2016, 16, 790.

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