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Advanced Mobile Edge Computing in 5G Networks
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Advanced Mobile Edge Computing in 5G Networks

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

Deadline for manuscript submissions: 25 March 2025 | Viewed by 5034

Special Issue Editors

Dr. Wanli Wen

E-Mail Website
Guest Editor
School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China
Interests: green communications; mobile/multi-access edge computing and caching; federated learning; holographic communications; physical layer security; semantic communications
Prof. Dr. Zheng Yang

E-Mail Website
Guest Editor
Centre for Research and Innovation in Software Engineering, Southwest University, Chongqing 400799, China
Interests: cryptography; privacy protection; blockchain; industrial Internet security; artificial intelligence security

Special Issue Information

Dear Colleagues,

In recent years, there have been a large number of technological breakthroughs and transformative applications driven by the mobile internet, especially 5G technology. Among them, mobile edge computing (MEC) has emerged as a key paradigm, leveraging the capabilities of 5G networks to revolutionize the way data are processed and utilized. The foundation laid by 5G technology, with its unprecedented bandwidth and ultra-low latency, provides fertile ground for MEC to flourish. By bringing computing resources closer to the data source, MEC minimizes latency and improves the overall user experience. The synergies between 5G and MEC offer myriad opportunities in various technical fields such as artificial intelligence (AI), machine learning (ML), autonomous vehicles, Blockchain, caching, smart sensing, semantic communications, and holographic communications. This dynamic convergence of 5G and MEC is reshaping the technology landscape, propelling us toward a future characterized by seamless connectivity, fast information processing, and an unparalleled level of interaction.

In this Special Issue, we seek submissions of original, completed, and unpublished work that is not presently under review by any other journal, magazine, or conference. We are particularly interested in the latest advancements and research findings pertaining to MEC in 5G networks. Topics of interest include, but are not limited to:

  • New computing architecture, algorithms, and protocols for MEC in 5G networks.
  • Recent advances in the integration of Blockchain and MEC in 5G networks.
  • Recent advances in the integration of AI/ML and MEC in 5G networks.
  • Recent advances in MEC-empowered autonomous vehicles in 5G networks.
  • Recent advances in the integration of communication, sensing, computation, and caching in 5G networks.
  • MEC for semantic communications.
  • MEC for holographic communications.

Dr. Wanli Wen
Prof. Dr. Zheng Yang
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

  • mobile edge computing (MEC)
  • 5G networks
  • artificial intelligence (AI)
  • machine learning (ML)
  • autonomous vehicles
  • blockchain
  • smart sensing
  • caching
  • semantic communications
  • holographic communications

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Published Papers (4 papers)

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Research

24 pages, 6162 KiB  
Article
Location Privacy Protection for the Internet of Things with Edge Computing Based on Clustering K-Anonymity
by Nanlan Jiang, Yinan Zhai, Yujun Wang, Xuesong Yin, Sai Yang and Pingping Xu
Sensors 2024, 24(18), 6153; https://doi.org/10.3390/s24186153 - 23 Sep 2024
Viewed by 667
Abstract
With the development of the Internet of Things (IoT) and edge computing, more and more devices, such as sensor nodes and intelligent automated guided vehicles (AGVs), can serve as edge devices to provide Location-Based Services (LBS) through the IoT. As the number of [...] Read more.
With the development of the Internet of Things (IoT) and edge computing, more and more devices, such as sensor nodes and intelligent automated guided vehicles (AGVs), can serve as edge devices to provide Location-Based Services (LBS) through the IoT. As the number of applications increases, there is an abundance of sensitive information in the communication process, pushing the focus of privacy protection towards the communication process and edge devices. The challenge lies in the fact that most traditional location privacy protection algorithms are not suited for the IoT with edge computing, as they primarily focus on the security of remote servers. To enhance the capability of location privacy protection, this paper proposes a novel K-anonymity algorithm based on clustering. This novel algorithm incorporates a scheme that flexibly combines real and virtual locations based on the requirements of applications. Simulation results demonstrate that the proposed algorithm significantly improves location privacy protection for the IoT with edge computing. When compared to traditional K-anonymity algorithms, the proposed algorithm further enhances the security of location privacy by expanding the potential region in which the real node may be located, thereby limiting the effectiveness of “narrow-region” attacks. Full article
(This article belongs to the Special Issue Advanced Mobile Edge Computing in 5G Networks)
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13 pages, 5263 KiB  
Article
Content-Aware Few-Shot Meta-Learning for Cold-Start Recommendation on Portable Sensing Devices
by Xiaomin Lv, Kai Fang and Tongcun Liu
Sensors 2024, 24(17), 5510; https://doi.org/10.3390/s24175510 - 26 Aug 2024
Viewed by 610
Abstract
The cold-start problem in sequence recommendations presents a critical and challenging issue for portable sensing devices. Existing content-aware approaches often struggle to effectively distinguish the relative importance of content features and typically lack generalizability when processing new data. To address these limitations, we [...] Read more.
The cold-start problem in sequence recommendations presents a critical and challenging issue for portable sensing devices. Existing content-aware approaches often struggle to effectively distinguish the relative importance of content features and typically lack generalizability when processing new data. To address these limitations, we propose a content-aware few-shot meta-learning (CFSM) model to enhance the accuracy of cold-start sequence recommendations. Our model incorporates a double-tower network (DT-Net) that learns user and item representations through a meta-encoder and a mutual attention encoder, effectively mitigating the impact of noisy data on auxiliary information. By framing the cold-start problem as few-shot meta-learning, we employ a model-agnostic meta-optimization strategy to train the model across a variety of tasks during the meta-learning phase. Extensive experiments conducted on three real-world datasets—ShortVideos, MovieLens, and Book-Crossing—demonstrate the superiority of our model in cold-start recommendation scenarios. Compared to MetaCs-DNN, the second-best approach, CFSM, achieves improvements of 1.55%, 1.34%, and 2.42% under the AUC metric on the three datasets, respectively. Full article
(This article belongs to the Special Issue Advanced Mobile Edge Computing in 5G Networks)
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17 pages, 2384 KiB  
Article
Enhanced FRER Mechanism in Time-Sensitive Networking for Reliable Edge Computing
by Shaoliu Hu, Yueping Cai, Shengkai Wang and Xiao Han
Sensors 2024, 24(6), 1738; https://doi.org/10.3390/s24061738 - 7 Mar 2024
Cited by 1 | Viewed by 1530
Abstract
Time-Sensitive Networking (TSN) and edge computing are promising networking technologies for the future of the Industrial Internet. TSN provides a reliable and deterministic low-latency communication service for edge computing. The Frame Replication and Elimination for Reliability (FRER) mechanism is important for improving the [...] Read more.
Time-Sensitive Networking (TSN) and edge computing are promising networking technologies for the future of the Industrial Internet. TSN provides a reliable and deterministic low-latency communication service for edge computing. The Frame Replication and Elimination for Reliability (FRER) mechanism is important for improving the network reliability of TSN. It achieves high reliability by transmitting identical frames in parallel on two disjoint paths, while eliminating duplicated frames at the destination node. However, there are two problems with the FRER mechanism. One problem is that it does not consider the path reliability, and the other one is that it is difficult to find two completely disjoint path pairs in some cases. To solve the above problems, this paper proposes a method to find edge-disjoint path pairs considering path reliability for FRER in TSN. The method includes two parts: one is building a reliability model for paths, and the other one is computing a working path and a redundant path with the Edge-Disjoint Path Pairs Selection (EDPPS) algorithm. Theoretical and simulation results show that the proposed method effectively improves path reliability while reducing the delay jitter of frames. Compared with the traditional FRER mechanism, the proposed method reduces delay jitter by 15.6% when the network load is 0.9. Full article
(This article belongs to the Special Issue Advanced Mobile Edge Computing in 5G Networks)
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13 pages, 8078 KiB  
Article
CNN and Attention-Based Joint Source Channel Coding for Semantic Communications in WSNs
by Xinyue Liu, Zhen Huang, Yulu Zhang, Yunjian Jia and Wanli Wen
Sensors 2024, 24(3), 957; https://doi.org/10.3390/s24030957 - 1 Feb 2024
Cited by 1 | Viewed by 1332
Abstract
Wireless Sensor Networks (WSNs) have emerged as an efficient solution for numerous real-time applications, attributable to their compactness, cost-effectiveness, and ease of deployment. The rapid advancement of 5G technology and mobile edge computing (MEC) in recent years has catalyzed the transition towards large-scale [...] Read more.
Wireless Sensor Networks (WSNs) have emerged as an efficient solution for numerous real-time applications, attributable to their compactness, cost-effectiveness, and ease of deployment. The rapid advancement of 5G technology and mobile edge computing (MEC) in recent years has catalyzed the transition towards large-scale deployment of WSN devices. However, the resulting data proliferation and the dynamics of communication environments introduce new challenges for WSN communication: (1) ensuring robust communication in adverse environments and (2) effectively alleviating bandwidth pressure from massive data transmission. In response to the aforementioned challenges, this paper proposes a semantic communication solution. Specifically, considering the limited computational and storage resources of WSN devices, we propose a flexible Attention-based Adaptive Coding (AAC) module. This module integrates window and channel attention mechanisms, dynamically adjusts semantic information in response to the current channel state, and facilitates adaptation of a single model across various Signal-to-Noise Ratio (SNR) environments. Furthermore, to validate the effectiveness of this approach, the paper introduces an end-to-end Joint Source Channel Coding (JSCC) scheme for image semantic communication, employing the AAC module. Experimental results demonstrate that the proposed scheme surpasses existing deep JSCC schemes across datasets of varying resolutions; furthermore, they validate the efficacy of the proposed AAC module, which is capable of dynamically adjusting critical information according to the current channel state. This enables the model to be trained over a range of SNRs and obtain better results. Full article
(This article belongs to the Special Issue Advanced Mobile Edge Computing in 5G Networks)
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