Special Issue "Machine Learning for Next-Generation Wireless Networks and Computing Systems"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: closed (10 December 2022) | Viewed by 1472

Special Issue Editors

Dr. Qiang Liu
E-Mail Website
Guest Editor
School of Computing, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
Interests: computer networks; wireless communication; machine learning; edge computing
Dr. Tao Han
E-Mail Website
Guest Editor
Helen and John C. Hartmann Department of Electrical and Computer Engineering, New Jersey Institute of Technology (NJIT), Newark, NJ 07102, USA
Interests: edge computing; mobile systems; augmented reality systems
Special Issues, Collections and Topics in MDPI journals
Dr. Haoxin Wang
E-Mail Website
Guest Editor
Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA
Interests: edge computing; autonomous vehicles; machine learning

Special Issue Information

Dear Colleagues,

Next-generation networks and computing systems aim to provide pervasive, extremely low-latency, ultra-reliable, and trustworthy communication and computation services for diverse use cases and services, including augmented and virtual reality (AR/VR), autonomous driving, vehicle-to-everything (V2X), internet of things (IoT), smart grids, industry 4.0 and precise agriculture. The exponential expansion of devices (e.g., phones, sensors, and vehicles), infrastructures (cellular and WiFi), and services extremely complicate the control and management of the system, which demands new approaches and methodologies.

The advances in machine learning in recent years, e.g., deep neural networks, deep learning, and deep reinforcement learning, show great potential for resolving the challenges under high-dimensional states and network dynamics. Machine learning can enable intelligent wireless communication (e.g., waveform, coding, and receivers), networking (e.g., congestion control and load balancing), and computing (e.g., scheduling and resource provision) in a broad range of scenarios. Nevertheless, the application of machine learning in next-generation networks and computing systems still needs further investigations in terms of robustness, scalability, availability, explainability, transformability, adaptability, and so on.

This Special Issue will focus on machine learning solutions to address the problems in next-generation networks and computing systems. The topics of interest include but are not limited to machine learning, especially deep learning and deep reinforcement learning, for resource management, congestion control, routing, signal processing, computation offloading, joint communication and sensing, and traffic modeling.

This Special Issue, “Machine Learning for Next-Generation Wireless Networks and Computing Systems”, will solicit research papers on various disciplines, including but not limited to the following:

  • machine learning for network resource management;
  • machine learning for physical layer wireless communication;
  • machine learning for transport-layer congestion control;
  • machine learning for computation offloading;
  • machine learning for traffic engineering;
  • machine learning for reconfigurable intelligent surface design;
  • machine learning for distributed resource provisioning;
  • machine learning for network slicing;
  • machine learning for quality-of-service (QoS) assurance;
  • machine learning for emerging applications, e.g., autonomous driving, augmented reality, and unmanned aerial vehicles;
  • machine learning for emerging scenarios, e.g., edge computing, precise agriculture, and smart cities.

Dr. Qiang Liu
Dr. Tao Han
Dr. Haoxin Wang
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. Electronics 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 2000 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
  • deep learning
  • deep reinforcement learning
  • resource management
  • routing
  • congestion control
  • wireless communication
  • edge computing

Published Papers (2 papers)

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Research

Article
Drone Detection Method Based on MobileViT and CA-PANet
Electronics 2023, 12(1), 223; https://doi.org/10.3390/electronics12010223 - 02 Jan 2023
Viewed by 529
Abstract
Aiming at the problems of the large amount of model parameters and false and missing detections of multi-scale drone targets, we present a novel drone detection method, YOLOv4-MCA, based on the lightweight MobileViT and Coordinate Attention. The proposed approach is improved according to [...] Read more.
Aiming at the problems of the large amount of model parameters and false and missing detections of multi-scale drone targets, we present a novel drone detection method, YOLOv4-MCA, based on the lightweight MobileViT and Coordinate Attention. The proposed approach is improved according to the framework of YOLOv4. Firstly, we use an improved lightweight MobileViT as the feature extraction backbone network, which can fully extract the local and global feature representations of the object and reduce the model’s complexity. Secondly, we adopt Coordinate Attention to improve PANet and to obtain a multi-scale attention called CA-PANet, which can obtain more positional information and promote the fusion of information with low- and high-dimensional features. Thirdly, we utilize the improved K-means++ method to optimize the object anchor box and improve the detection efficiency. At last, we construct a drone dataset and conduct a performance experiment based on the Mosaic data augmentation method. The experimental results show that the mAP of the proposed approach reaches 92.81%, the FPS reaches 40 f/s, and the number of parameters is only 13.47 M, which is better than mainstream algorithms and achieves a high detection accuracy for multi-scale drone targets using a low number of parameters. Full article
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Article
Mobile Anchor and Kalman Filter Boosted Bounding Box for Localization in Wireless Sensor Networks
Electronics 2022, 11(20), 3296; https://doi.org/10.3390/electronics11203296 - 13 Oct 2022
Viewed by 464
Abstract
Event detection is usually the primary purpose of wireless sensor networks (WSNs). Therefore, it is crucial to determine where and when an event occurs in order to map the event to its spatio-temporal domain. In WSN localization, a few anchor nodes are those [...] Read more.
Event detection is usually the primary purpose of wireless sensor networks (WSNs). Therefore, it is crucial to determine where and when an event occurs in order to map the event to its spatio-temporal domain. In WSN localization, a few anchor nodes are those aware of their locations via the Global Positioning System (GPS), which is energy-consuming. Non-anchor nodes self-localize by gathering information from anchor nodes to estimate their positions using a localization technique. Traditional algorithms use at least three static anchors for the localization process. Recently, researchers opted to replace multiple static anchors by a single mobile anchor during the localization process. This paper proposes a Kalman filter based on bounding box localization algorithm (KF-BBLA) in WSNs with mobile anchor node. We present a new mobile anchor localization strategy to minimize energy, hardware costs, and computation complexity, while improving accuracy and cost-effectiveness. Network connectivity measurement and the bounding box localization method are used in order to identify the bounded possible localization zone. The Kalman filter is then used to minimize the uncertainty produced by the connectivity process. We aim also to minimize the localization inaccuracies generated by the bounding box algorithm. Simulation results show that our proposed approach significantly reduces the localization error compared to other localization algorithms chosen from the recent literature by up to 20%. We use the cumulative distribution function (CDF) as an indicator to assess the accuracy of our proposed algorithm. Full article
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