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Recent Advance in Mobile Edge Computing and Wireless Communication Technology

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

Deadline for manuscript submissions: closed (20 February 2023) | Viewed by 7746

Special Issue Editors


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Guest Editor
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Interests: high-performance computing; heterogeneous intelligent computing; edge computing; mobile edge computing

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Guest Editor
Associate Professor, School of Computer Science and Technology (School of Cyberspace Security), Xinjiang University, Urumqi 830046, China
Interests: wireless optical communication and networking; visible light communication; free space optical communication; the fusion of light and wireless; the basic theory and key technologies of the fifth and sixth generation mobile communication (5G and 6G)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, with the rise of Internet of Things devices and the development of mobile communication networks, the current society has become increasingly dependent on data-centric life, resulting in explosive growth in the number of wireless devices. By the end of 2023, Cisco predicts that the number of mobile devices will reach 13.1 billion, and the number of Internet-enabled devices will have increased from 18.4 billion in 2018 to 29.3 billion. Mobile communication networks are under enormous pressure because of the diversification of wireless services, the surge in user traffic and the high user experience requirements. The emergence of mobile edge computing has effectively alleviated this pressure. Mobile edge computing focuses on sinking service platforms with computing, storage, and communications capabilities to the edge of the network, and require that mobile users offload their computationally intensive tasks to the mobile edge computing facilities. Mobile edge computing emphasizes proximity to mobile users in order to reduce latency in network operations and service delivery. Although mobile edge computing can fulfill the high computational requirements of users, it takes more energy and delay to offload tasks to the server. To better utilize the advantages of mobile edge computing, some methods are needed to reduce the delay and energy consumption. this Special Issue encourages authors from academia and industry to submit original research papers related to advanced technological innovation for mobile edge computing and wireless communication networks.

The topics of this Issue include but are not limited to the following:

  • Mobile edge computing resource management;
  • Cooperative mobile edge computing;
  • Distributed and centralized machine learning algorithms in mobile edge computing;
  • Mobile edge computing and wireless communication technology;
  • Low-consumption and energy-saving mobile edge computing;
  • Cloud computing and mobile edge computing;
  • Mobile edge computing security and privacy protection

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. Juan Fang
Dr. Jupeng Ding
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 (3 papers)

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Research

19 pages, 955 KiB  
Article
A Power Allocation Scheme for MIMO-NOMA and D2D Vehicular Edge Computing Based on Decentralized DRL
by Dunxing Long, Qiong Wu, Qiang Fan, Pingyi Fan, Zhengquan Li and Jing Fan
Sensors 2023, 23(7), 3449; https://doi.org/10.3390/s23073449 - 25 Mar 2023
Cited by 9 | Viewed by 2188
Abstract
In vehicular edge computing (VEC), some tasks can be processed either locally or on the mobile edge computing (MEC) server at a base station (BS) or a nearby vehicle. In fact, tasks are offloaded or not, based on the status of vehicle-to-infrastructure (V2I) [...] Read more.
In vehicular edge computing (VEC), some tasks can be processed either locally or on the mobile edge computing (MEC) server at a base station (BS) or a nearby vehicle. In fact, tasks are offloaded or not, based on the status of vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication. In this paper, device-to-device (D2D)-based V2V communication and multiple-input multiple-output and nonorthogonal multiple access (MIMO-NOMA)-based V2I communication are considered. In actual communication scenarios, the channel conditions for MIMO-NOMA-based V2I communication are uncertain, and the task arrival is random, leading to a highly complex environment for VEC systems. To solve this problem, we propose a power allocation scheme based on decentralized deep reinforcement learning (DRL). Since the action space is continuous, we employ the deep deterministic policy gradient (DDPG) algorithm to obtain the optimal policy. Extensive experiments demonstrate that our proposed approach with DRL and DDPG outperforms existing greedy strategies in terms of power consumption and reward. Full article
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10 pages, 5122 KiB  
Article
A LTCC-Based Ku-Band 8-Channel T/R Module Integrated with Drive Amplification and 7-Bit True-Time-Delay
by Xiao Liu, Qinghua Zeng, Zhengzhi Ding and Haitao Xu
Sensors 2022, 22(17), 6568; https://doi.org/10.3390/s22176568 - 31 Aug 2022
Viewed by 1680
Abstract
Ku-band drive amplification and a 7-bit true-time-delay (TTD) function were realized as a part of a LTCC-based T/R module to increase integration. The 8-channel T/R module was fabricated and its key characteristics were measured, including a 3-bit (1/2/4 λ) TTD, 4-bit (0.25/0.5/1/2 λ) [...] Read more.
Ku-band drive amplification and a 7-bit true-time-delay (TTD) function were realized as a part of a LTCC-based T/R module to increase integration. The 8-channel T/R module was fabricated and its key characteristics were measured, including a 3-bit (1/2/4 λ) TTD, 4-bit (0.25/0.5/1/2 λ) TTD, receive gain, noise figure and output power. The 8-channel T/R module can be further adopted to increase bandwidth and scanning angle of phased arrays without beam squint. Full article
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24 pages, 5272 KiB  
Article
Aggregation Strategy on Federated Machine Learning Algorithm for Collaborative Predictive Maintenance
by Ali Bemani and Niclas Björsell
Sensors 2022, 22(16), 6252; https://doi.org/10.3390/s22166252 - 19 Aug 2022
Cited by 19 | Viewed by 3270
Abstract
Industry 4.0 lets the industry build compact, precise, and connected assets and also has made modern industrial assets a massive source of data that can be used in process optimization, defining product quality, and predictive maintenance (PM). Large amounts of data are collected [...] Read more.
Industry 4.0 lets the industry build compact, precise, and connected assets and also has made modern industrial assets a massive source of data that can be used in process optimization, defining product quality, and predictive maintenance (PM). Large amounts of data are collected from machines, processed, and analyzed by different machine learning (ML) algorithms to achieve effective PM. These machines, assumed as edge devices, transmit their data readings to the cloud for processing and modeling. Transmitting massive amounts of data between edge and cloud is costly, increases latency, and causes privacy concerns. To address this issue, efforts have been made to use edge computing in PM applications., reducing data transmission costs and increasing processing speed. Federated learning (FL) has been proposed a mechanism that provides the ability to create a model from distributed data in edge, fog, and cloud layers without violating privacy and offers new opportunities for a collaborative approach to PM applications. However, FL has challenges in confronting with asset management in the industry, especially in the PM applications, which need to be considered in order to be fully compatible with these applications. This study describes distributed ML for PM applications and proposes two federated algorithms: Federated support vector machine (FedSVM) with memory for anomaly detection and federated long-short term memory (FedLSTM) for remaining useful life (RUL) estimation that enables factories at the fog level to maximize their PM models’ accuracy without compromising their privacy. A global model at the cloud level has also been generated based on these algorithms. We have evaluated the approach using the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) dataset to predict engines’ RUL Experimental results demonstrate the advantage of FedSVM and FedLSTM in terms of model accuracy, model convergence time, and network usage resources. Full article
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