See the published version of this article at Journal of Internet Services and Applications.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
A Survey on Data Analysis on Large-Scale Wireless Networks: Online Stream Processing, Trends, and Challenges
Dianne Scherly Varela de Medeiros
Universidade Federal Fluminense
Corresponding Author
ORCiD: https://orcid.org/0000-0002-0361-5903
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at Journal of Internet Services and Applications.
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.
Keywords
Stream Processing, Big Data, Wireless, Data Mining
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
Version 2
Posted 16 Sep, 2020
Published
On 19 Oct, 2020
Published
On 19 Oct, 2020
No community comments so far
Reviewer #2 agreed
On 18 Sep, 2020
Review #2 received
Received 18 Sep, 2020
Editorial decision: Accept
On 18 Sep, 2020
Review #1 received
Received 17 Sep, 2020
Reviewer #1 agreed
On 15 Sep, 2020
Reviewers invited
Invitations sent on 14 Sep, 2020
Editor assigned
On 13 Sep, 2020
Submission checks complete
On 12 Sep, 2020
Editor invited
On 12 Sep, 2020
No comments provided
Editorial decision: Major revision
On 27 Aug, 2020
Review #1 received
Received 18 Aug, 2020
Review #2 received
Received 18 Aug, 2020
Reviewer #3 agreed
On 09 Aug, 2020
Reviewer #1 agreed
On 04 Aug, 2020
Reviewer #2 agreed
On 04 Aug, 2020
Reviewers invited
Invitations sent on 03 Aug, 2020
Editor assigned
On 17 Mar, 2020
First submitted
On 16 Mar, 2020
Submission checks complete
On 16 Mar, 2020
Editor invited
On 16 Mar, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Computer Architecture and Engineering
Learn more about our company.
This preprint is under consideration at Journal of Internet Services and Applications. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
A Survey on Data Analysis on Large-Scale Wireless Networks: Online Stream Processing, Trends, and Challenges
Dianne Scherly Varela de Medeiros
Universidade Federal Fluminense
Corresponding Author
ORCiD: https://orcid.org/0000-0002-0361-5903
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
Version 2
Posted 16 Sep, 2020
Published
On 19 Oct, 2020
Published
On 19 Oct, 2020
No community comments so far
Reviewer #2 agreed
On 18 Sep, 2020
Review #2 received
Received 18 Sep, 2020
Editorial decision: Accept
On 18 Sep, 2020
Review #1 received
Received 17 Sep, 2020
Reviewer #1 agreed
On 15 Sep, 2020
Reviewers invited
Invitations sent on 14 Sep, 2020
Editor assigned
On 13 Sep, 2020
Submission checks complete
On 12 Sep, 2020
Editor invited
On 12 Sep, 2020
No comments provided
Editorial decision: Major revision
On 27 Aug, 2020
Review #1 received
Received 18 Aug, 2020
Review #2 received
Received 18 Aug, 2020
Reviewer #3 agreed
On 09 Aug, 2020
Reviewer #1 agreed
On 04 Aug, 2020
Reviewer #2 agreed
On 04 Aug, 2020
Reviewers invited
Invitations sent on 03 Aug, 2020
Editor assigned
On 17 Mar, 2020
First submitted
On 16 Mar, 2020
Submission checks complete
On 16 Mar, 2020
Editor invited
On 16 Mar, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Computer Architecture and Engineering
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at Journal of Internet Services and Applications.
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.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.