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Frontiers in Mobile Multimedia Communications
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Frontiers in Mobile Multimedia Communications

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

Deadline for manuscript submissions: closed (30 March 2023) | Viewed by 4515

Special Issue Editors

Prof. Dr. Yun Lin

E-Mail Website
Guest Editor
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
Interests: fault detection and recognition; machine learning and data analytics over wireless networks; signal processing and analysis; cognitive radio and software defined radio; artificial intelligence; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Pengfei Jiao

E-Mail Website
Guest Editor
School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, China
Interests: graph machine learning; complex network analysis; data mining; cyberspace security
Dr. Danda B. Rawat

E-Mail Website
Guest Editor
Department of Electrical Engineering and Computer Science, Howard University, Washington, DC 20059, USA
Interests: machine learning; wireless networking; cyber-physical systems; Internet-of-Things; smart cities; software defined systems; sensor networks; vehicular networks
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shiwen Mao

E-Mail Website
Guest Editor
School of Cyberspace, Auburn University, Auburn, AL, USA
Interests: wireless networks; multimedia communications; smart grid

Special Issue Information

Dear Colleagues,

With the commercialization of 5G communication technology, multimedia services and their applications in the mobile environment have grown at an extraordinary rate. Therefore, it is the right time to implement a more widespread use of novel mobile multimedia communication technologies, such as content features analysis and coding, media access control, multimedia flow and error control, cross-layer optimization, Quality of Experience (QoE), media cloud, as well as mobility management and security application. These are all important problems that can only be solved through more extensive research.

Therefore, more effective thoughts and methods are needed to enhance the theory and applications of mobile multimedia communication. Within this scope, our Special Issue provides a new platform for researchers from universities, industries and research institutes to publish their research on new technologies, applications and standards. Original unpublished papers that contributes to improving mobile multimedia communication, both in theory and in practice, are welcome.

  • Flexible architecture in AI enabled software-defined networks;
  • Algorithms, architecture, applications of mobile multimedia communication;
  • Computational framework and structure for big data;
  • behavior understanding and object tracking in big multimedia data;
  • Localization, positioning and tracking techniques;
  • Security and privacy management of Internet of Multimedia Things (IoMT);
  • Multimedia-based signal processing;
  • Mobile crowdsensing;
  • Social network mining and recommendation;
  • Big audio and acoustic signal data processing.

If you want to learn more information or need any advice, you can contact the Special Issue Editor Penelope Wang via <penelope.wang@mdpi.com> directly.

Prof. Dr. Yun Lin
Prof. Dr. Pengfei Jiao
Dr. Danda B. Rawat
Prof. Dr. Shiwen Mao
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.

Published Papers (2 papers)

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Research

16 pages, 457 KiB  
Article
An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment
by Lifeng Lei, Liang Kou, Xianghao Zhan, Jilin Zhang and Yongjian Ren
Sensors 2022, 22(19), 7436; https://doi.org/10.3390/s22197436 - 30 Sep 2022
Cited by 7 | Viewed by 2444
Abstract
With the advent of the digital information age, new data services such as virtual reality, industrial Internet, and cloud computing have proliferated in recent years. As a result, it increases operator demand for 5G bearer networks by providing features such as high transmission [...] Read more.
With the advent of the digital information age, new data services such as virtual reality, industrial Internet, and cloud computing have proliferated in recent years. As a result, it increases operator demand for 5G bearer networks by providing features such as high transmission capacity, ultra-long transmission distance, network slicing, and intelligent management and control. Software-defined networking, as a new network architecture, intends to increase network flexibility and agility and can better satisfy the demands of 5G networks for network slicing. Nevertheless, software-defined networking still faces the challenge of network intrusion. We propose an abnormal traffic detection method based on the stacking method and self-attention mechanism, which makes up for the shortcoming of the inability to track long-term dependencies between data samples in ensemble learning. Our method utilizes a self-attention mechanism and a convolutional network to automatically learn long-term associations between traffic samples and provide them to downstream tasks in sample embedding. In addition, we design a novel stacking ensemble method, which computes the sample embedding and the predicted values of the heterogeneous base learner through the fusion module to obtain the final outlier results. This paper conducts experiments on abnormal traffic datasets in the software-defined network environment, calculates precision, recall and F1-score, and compares and analyzes them with other algorithms. The experimental results show that the method designed in this paper achieves 0.9972, 0.9996, and 0.9984 in multiple indicators of precision, recall, and F1-score, respectively, which are better than the comparison methods. Full article
(This article belongs to the Special Issue Frontiers in Mobile Multimedia Communications)
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25 pages, 2816 KiB  
Article
Dissecting Latency in 360° Video Camera Sensing Systems
by Zhisheng Yan and Jun Yi
Sensors 2022, 22(16), 6001; https://doi.org/10.3390/s22166001 - 11 Aug 2022
Cited by 4 | Viewed by 1520
Abstract
360° video camera sensing is an increasingly popular technology. Compared with traditional 2D video systems, it is challenging to ensure the viewing experience in 360° video camera sensing because the massive omnidirectional data introduce adverse effects on start-up delay, event-to-eye delay, and frame [...] Read more.
360° video camera sensing is an increasingly popular technology. Compared with traditional 2D video systems, it is challenging to ensure the viewing experience in 360° video camera sensing because the massive omnidirectional data introduce adverse effects on start-up delay, event-to-eye delay, and frame rate. Therefore, understanding the time consumption of computing tasks in 360° video camera sensing becomes the prerequisite to improving the system’s delay performance and viewing experience. Despite the prior measurement studies on 360° video systems, none of them delves into the system pipeline and dissects the latency at the task level. In this paper, we perform the first in-depth measurement study of task-level time consumption for 360° video camera sensing. We start with identifying the subtle relationship between the three delay metrics and the time consumption breakdown across the system computing task. Next, we develop an open research prototype Zeus to characterize this relationship in various realistic usage scenarios. Our measurement of task-level time consumption demonstrates the importance of the camera CPU-GPU transfer and the server initialization, as well as the negligible effect of 360° video stitching on the delay metrics. Finally, we compare Zeus with a commercial system to validate that our results are representative and can be used to improve today’s 360° video camera sensing systems. Full article
(This article belongs to the Special Issue Frontiers in Mobile Multimedia Communications)
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