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Sensors
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Machine Learning Models for Wireless Network Monitoring and Data Analysis
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Machine Learning Models for Wireless Network Monitoring and Data Analysis

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (30 May 2020) | Viewed by 12317

Special Issue Editors

Prof. Dr. Young-June Choi

E-Mail Website
Guest Editor
Department of Software, Ajou University, Suwon, South Korea
Interests: machine learning; artificial intelligence; data analysis; network security; radio resource management; industrial IoT; V2X
Special Issues, Collections and Topics in MDPI journals
Dr. Jing Ma

E-Mail Website
Guest Editor
Science University of Tokyo, Tokyo 162-8601, Japan
Interests: machine learning, artificial intelligence, chaos, quantum neural networks, IoT, cognitive radio networks, WLAN, V2X, power packet networks

Special Issue Information

Dear Colleagues,

Machine learning has been extensively studied for data analysis of many domains, including wireless networking. Especially for 5G networks, mission-critical IoT service such as autonomous driving is expected to launch, where machine learning models are needed for communication, security, and resource management. This Special Issue is intended to provide a discussion of recent contributions on wireless network monitoring and data analysis based on machine learning as well as artificial intelligence.

Prof. Dr. Young-June Choi
Dr. Jing Jing Ma
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Machine learning;
  • Artificial intelligence;
  • Network data analysis;
  • Network management;
  • Network security;
  • Wireless communications.

Published Papers (3 papers)

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Research

15 pages, 5955 KiB  
Article
Wi-Fi CSI-Based Outdoor Human Flow Prediction Using a Support Vector Machine
by Masakatsu Ogawa and Hirofumi Munetomo
Sensors 2020, 20(7), 2141; https://doi.org/10.3390/s20072141 - 10 Apr 2020
Cited by 6 | Viewed by 3558
Abstract
This paper proposes a channel state information (CSI)-based prediction method of a human flow that includes activity. The objective of the paper is to predict a human flow in an outdoor road. This human flow prediction is useful for the prediction of the [...] Read more.
This paper proposes a channel state information (CSI)-based prediction method of a human flow that includes activity. The objective of the paper is to predict a human flow in an outdoor road. This human flow prediction is useful for the prediction of the number of passing people and their activity without privacy issues as a result of the absence of any camera systems. In this paper, we assume seven types of activities: one, two, and three people walking; one, two, and three people running; and one person cycling. Since the CSI can effectively express the effect of multipath fading in wireless signals, we expected the CSI to predict the various activities. In our proposed method, the amplitude and phase components are extracted from the measured CSI. The feature values for machine learning are the mean and variance of the maximum eigenvalue derived from the auto-correlation matrix and variance–covariance matrix composed of the amplitude or phase components and the passing time of flow. Using these feature values, we evaluated the prediction accuracy by leave-one-out cross-validation with a linear support vector machine (SVM). As a result, the proposed method achieved the maximum prediction accuracy of 100% for each direction and 99.5% for two directions. Full article
(This article belongs to the Special Issue Machine Learning Models for Wireless Network Monitoring and Data Analysis)
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23 pages, 5564 KiB  
Article
Path Loss Prediction Based on Machine Learning Techniques: Principal Component Analysis, Artificial Neural Network, and Gaussian Process
by Han-Shin Jo, Chanshin Park, Eunhyoung Lee, Haing Kun Choi and Jaedon Park
Sensors 2020, 20(7), 1927; https://doi.org/10.3390/s20071927 - 30 Mar 2020
Cited by 83 | Viewed by 5937
Abstract
Although various linear log-distance path loss models have been developed for wireless sensor networks, advanced models are required to more accurately and flexibly represent the path loss for complex environments. This paper proposes a machine learning framework for modeling path loss using a [...] Read more.
Although various linear log-distance path loss models have been developed for wireless sensor networks, advanced models are required to more accurately and flexibly represent the path loss for complex environments. This paper proposes a machine learning framework for modeling path loss using a combination of three key techniques: artificial neural network (ANN)-based multi-dimensional regression, Gaussian process-based variance analysis, and principle component analysis (PCA)-aided feature selection. In general, the measured path loss dataset comprises multiple features such as distance, antenna height, etc. First, PCA is adopted to reduce the number of features of the dataset and simplify the learning model accordingly. ANN then learns the path loss structure from the dataset with reduced dimension, and Gaussian process learns the shadowing effect. Path loss data measured in a suburban area in Korea are employed. We observe that the proposed combined path loss and shadowing model is more accurate and flexible compared to the conventional linear path loss plus log-normal shadowing model. Full article
(This article belongs to the Special Issue Machine Learning Models for Wireless Network Monitoring and Data Analysis)
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16 pages, 3616 KiB  
Article
Intelligent Lecturer Tracking and Capturing System Based on Face Detection and Wireless Sensing Technology
by Tan-Hsu Tan, Tien-Ying Kuo and Huibin Liu
Sensors 2019, 19(19), 4193; https://doi.org/10.3390/s19194193 - 27 Sep 2019
Cited by 11 | Viewed by 2396
Abstract
In this paper, we propose an intelligent lecturer tracking and capturing (ILTC) system to automatically record course videos. Real-time and stable lecturer localization is realized by combining face detection with infrared (IR) thermal sensors, preventing detection failure caused by abrupt and rapid movements [...] Read more.
In this paper, we propose an intelligent lecturer tracking and capturing (ILTC) system to automatically record course videos. Real-time and stable lecturer localization is realized by combining face detection with infrared (IR) thermal sensors, preventing detection failure caused by abrupt and rapid movements in face detection and solving the non-real-time sensing problem for IR thermal sensors. Further, the camera is panned automatically by a servo motor controlled with a microcontroller to keep the lecturer in the center of the screen. Experiments were conducted in a classroom and a laboratory. Experimental results demonstrated that the accuracy of the proposed system is much higher than that of the system without IR thermal sensors. The survey of 32 teachers from two universities showed that the proposed system is a more practical utility and meets the demand of increasing online courses. Full article
(This article belongs to the Special Issue Machine Learning Models for Wireless Network Monitoring and Data Analysis)
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