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Data-driven design of intelligent wireless networks: an overview and tutorial

Merima Kulin (UGent) , Carolina Fortuna (UGent) , Eli De Poorter (UGent) , Dirk Deschrijver (UGent) and Ingrid Moerman (UGent)
(2016) SENSORS. 16(6).
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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.
Keywords
intelligent systems, cognitive networking, knowledge discovery, machine learning, COGNITIVE RADIO NETWORKS, MACHINE-LEARNING TECHNIQUES, DATA MINING TECHNIQUES, SENSOR NETWORKS, NEURAL-NETWORKS, COMPUTATIONAL INTELLIGENCE, CLUSTERING ALGORITHMS, PHYSICAL-ACTIVITY, CLASSIFICATION, LAYER, wireless networks, data science, data-driven research, IBCN

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Citation

Please use this url to cite or link to this publication:

Chicago
Kulin, Merima, Carolina Fortuna, Eli De Poorter, Dirk Deschrijver, and Ingrid Moerman. 2016. “Data-driven Design of Intelligent Wireless Networks: An Overview and Tutorial.” Sensors 16 (6).
APA
Kulin, M., Fortuna, C., De Poorter, E., Deschrijver, D., & Moerman, I. (2016). Data-driven design of intelligent wireless networks: an overview and tutorial. SENSORS, 16(6).
Vancouver
1.
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(6).
MLA
Kulin, Merima, Carolina Fortuna, Eli De Poorter, et al. “Data-driven Design of Intelligent Wireless Networks: An Overview and Tutorial.” SENSORS 16.6 (2016): n. pag. Print.
@article{8123493,
  abstract     = {Data science or {\textacutedbl}data-driven research{\textacutedbl} 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.},
  articleno    = {790},
  author       = {Kulin, Merima and Fortuna, Carolina and De Poorter, Eli and Deschrijver, Dirk and Moerman, Ingrid},
  issn         = {1424-8220},
  journal      = {SENSORS},
  keyword      = {intelligent systems,cognitive networking,knowledge discovery,machine learning,COGNITIVE RADIO NETWORKS,MACHINE-LEARNING TECHNIQUES,DATA MINING TECHNIQUES,SENSOR NETWORKS,NEURAL-NETWORKS,COMPUTATIONAL INTELLIGENCE,CLUSTERING ALGORITHMS,PHYSICAL-ACTIVITY,CLASSIFICATION,LAYER,wireless networks,data science,data-driven research,IBCN},
  language     = {eng},
  number       = {6},
  title        = {Data-driven design of intelligent wireless networks: an overview and tutorial},
  url          = {http://dx.doi.org/10.3390/s16060790},
  volume       = {16},
  year         = {2016},
}

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