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Applied Sciences
Special Issues
Cooperative Data Management and Learning Analytics in Mobile Edge Computing
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Cooperative Data Management and Learning Analytics in Mobile Edge Computing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 March 2024) | Viewed by 1669

Special Issue Editors

Prof. Dr. Yunliang Chen

E-Mail Website
Guest Editor
School of Computer Science, China University of Geosciences, Wuhan 430074, China
Interests: edge computing; cloud computing; data mining; IoT
Special Issues, Collections and Topics in MDPI journals
Dr. Jianxin Li

E-Mail Website
Guest Editor
School of Information Technology, Deakin University, Burwood, VIC 3125, Australia
Interests: social computing; query processing and optimization; big data analytics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Geyong Min

E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter EX4 4SB, UK
Interests: computer networks; wireless communications; parallel and distributed computing; ubiquitous computing; multimedia systems; modeling and performance engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Edge computing has recently become a promising and active research area that can support the interaction between mobile vehicles and other infrastructure systems. It has led to advances in software platforms that deliver highly improved services for many attractive applications such as traffic monitoring and prediction, utility monitoring, and emergency management services. Most existing work has mainly focused on the general framework design and computation offloading of edge computing systems. However, data management also plays a key role in edge computing as it involves a wide range of processes, including data collection, storage, access, and analysis, for effective information processing and knowledge discovery of a heterogenous and sheer amount of mobile edge data that are continuously generated by data producers throughout the lifecycle. In particular, under various application scenarios, there is a strong requirement for the cooperative capabilities of data management and learning analytics on edge computing platforms.

To explore these under-explored aspects, it is highly important to comprehensively review and investigate the latest breakthroughs and designs of cooperative data management and learning analytics for mobile edge computing, including software modeling, distributed data management theories and algorithms, and new emerging applications. Moreover, building advanced edge computing architectures and inspiring new research directions are of prominent importance in cooperative mobile edge computing intelligence. This Special Issue will offer a timely collection of relevant and original contributions that benefit the researchers and practitioners in the research fields of distributed data management and learning analytics for both cooperative edge computing mobile software and systems. The current research problems within the topic of applied sciences should be of interest to the recent edge computing and mobile software communities.

Topics of interest include, but are not limited to:

  • AI-enabled approaches for mobile edge computing;
  • Distributed computing architectures for mobile edge data learning analytics;
  • Data lifecycle management in mobile edge computing;
  • Novel theories, concepts, and paradigms for mobile edge data management and learning analytics;
  • New methods and techniques for cooperative mobile edge data management and learning analytics;
  • Tools and platforms for cooperative mobile edge data management and learning analytics;
  • System designs for cooperative mobile edge data management and learning analytics;
  • Advanced methods and techniques for mobile edge data quality management;
  • Advanced methods, techniques, and architectures for data security and privacy in mobile edge computing;
  • Applications and case studies for mobile edge computing.

Prof. Dr. Yunliang Chen
Dr. Jianxin Li
Prof. Dr. Geyong Min
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. Applied Sciences 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 2400 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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16 pages, 1973 KiB  
Article
Automatically Expanding User-Management System for Massive Users in the Cloud Platform
by Shengyang Li, Zhen Wang and Wanfeng Zhang
Appl. Sci. 2024, 14(6), 2549; https://doi.org/10.3390/app14062549 - 18 Mar 2024
Viewed by 400
Abstract
Cloud computing has become one of the key technologies used for big data processing and analytics. User management on cloud platforms is a growing challenge as the number of users and the complexity of systems increase. In light of the user-management system provided [...] Read more.
Cloud computing has become one of the key technologies used for big data processing and analytics. User management on cloud platforms is a growing challenge as the number of users and the complexity of systems increase. In light of the user-management system provided by major cloud service providers, which cannot manage multiple types of user systems, this article proposed scale-out automated expansion user management for authorization synchronization to improve the efficiency and scalability of user management on cloud platforms. Three modules for user-automated expansion were designed and implemented to synchronize the authentication information from the cloud platform resource user to the data-processing user. Additionally, an optimized dynamically weighted load-balancing algorithm in Nginx is presented in this article that adjusts the weight according to load information such as CPU and memory usage, and a better load balance can be achieved. The effectiveness of the proposed user-management system is substantiated by comparing it with two existing infrastructures, including multiple data centers and the Huawei cloud platform. The experimental results validate the finding that scale-out automated expansion user management across the Huawei cloud platform can effectively synchronize data accessing authority with cloud resource utilizing authority. Furthermore, the optimized weighted load-balancing algorithm is also valuable for massive concurrent user registration based on limited cloud resources. In the future, this scale-out user-management system could be applied to other cloud platforms and extended by database synchronization to satisfy the needs of data sharing among multiple types of users belonging to different cloud platforms. Full article
(This article belongs to the Special Issue Cooperative Data Management and Learning Analytics in Mobile Edge Computing)
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17 pages, 2145 KiB  
Article
Multi-Scale Spatial–Temporal Transformer: A Novel Framework for Spatial–Temporal Edge Data Prediction
by Junhao Ming, Dongmei Zhang and Wei Han
Appl. Sci. 2023, 13(17), 9651; https://doi.org/10.3390/app13179651 - 25 Aug 2023
Viewed by 688
Abstract
Spatial–temporal prediction is an important part of a great number of applications, such as urban traffic control, urban traffic management, and urban traffic planning. However, real-world spatial–temporal data often have complex patterns, so it is still challenging to predict them accurately. Most existing [...] Read more.
Spatial–temporal prediction is an important part of a great number of applications, such as urban traffic control, urban traffic management, and urban traffic planning. However, real-world spatial–temporal data often have complex patterns, so it is still challenging to predict them accurately. Most existing spatial–temporal prediction models fail to aggregate the spatial features in a suitable neighborhood during fixed spatial dependencies extraction and lack adequately comprehensive time series analysis for intricate temporal dependencies. This paper proposes a novel model named multi-scale spatial–temporal transformer network (MSSTTN) to deal with intricate spatial–temporal patterns. Firstly, we develop an improved graph wavelet neural network, which learns how to pass the spatial graph signals of different frequency scales to adjust the neighborhood of feature aggregation adaptively. Then, we propose decomposing the time series into local trend-cyclical parts of various scales during time series analysis, making the model capture more reliable temporal dependencies. The proposed model has been evaluated on publicly available real-world datasets. The experimental findings indicate that the proposed model exhibits superior performance compared to conventional techniques including, spatial–temporal transformer (STTNs), GraphWaveNet, and others. Full article
(This article belongs to the Special Issue Cooperative Data Management and Learning Analytics in Mobile Edge Computing)
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