Future Technologies for Data Management, Processing and Application, 2nd Edition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 July 2026 | Viewed by 459

Special Issue Editors


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Guest Editor
Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
Interests: wireless ad hoc and sensor networks; intelligent in-ternet of things; network softwarization; medical image processing
Special Issues, Collections and Topics in MDPI journals
Convergence Research Institute, Sungkyunkwan University, Suwon 16419, Republic of Korea
Interests: internet of things; data aggregation; time-series data; graph neural networks

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Guest Editor
James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
Interests: edge AI; autonomous UAV navigation; zero-touch network orchestration

Special Issue Information

Dear Colleagues,

Over the past few years, we have experienced the intelligent convergence of various technologies, and the most important factor in the centre of this has been data. Various business models exploit the importance of data in their favour, and it is safe to say that data has become the biggest commodity of the technological world, with current trends only suggesting an increase in its importance. Data can originate from any technological domain, but its use in that domain is determined by its processing and analysis. One of the major challenges in harnessing the boundless capabilities of data is effective management to enable generalized and targeted processing for certain types of applications. This involves converging diverse topics like information management, intelligent information processing, interaction management, and networking.

The Special Issue represents an open call for investigators to contribute original research articles as well as review articles that will stimulate continuing efforts from researchers in both academia and industry to present novel approaches, algorithms and applications on data management and processing for innovative services and new paradigms. Topics of interest may include, but are not limited to the following:

  • Data management for the Internet of Things and sensor systems;
  • Mobile cloud computing and data management;
  • Context-aware computing for intelligent mobile services;
  • Data-stream processing in mobile/sensor networks;
  • Personalized routing, eco-routing, and routing for mobile networks;
  • Data-intensive mobile computing;
  • Streams, sensor networks, and complex event processing;
  • Innovative applications driven by mobile data;
  • Mobile data analytics;
  • Applications and challenges of in-network computation;
  • Data management for connected cars, intelligent transportation systems, and smart spaces;
  • Privacy, trust and security in databases;
  • Data fusion and integration;
  • Knowledge discovery, clustering, and data mining;
  • Machine learning for data management and vice versa.

Prof. Dr. Duc Tai Le
Dr. Van-Vi Vo
Dr. Syed Muhammad Raza
Guest Editors

Manuscript Submission Information

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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 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.

Keywords

  • data mining
  • mobile networks
  • sensor networks
  • data management
  • data security
  • data fusion and integration

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Published Papers (1 paper)

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Research

17 pages, 377 KB  
Article
Fractional–Temporal Lorentz Graph Networks: Integrating Physical Memory into Dynamic Knowledge Reasoning
by Xinyuan Chen, Norshaharizan Puteh and Mohd Nizam Husen
Electronics 2026, 15(9), 1919; https://doi.org/10.3390/electronics15091919 - 1 May 2026
Viewed by 206
Abstract
Dynamic knowledge representation in curved manifolds conventionally relies on integer-order Markovian sequence encoders, intrinsically yielding exponential memory decay. This paradigm fails to model the anomalous diffusion and heavy-tailed historical dependencies inherent in complex evolutionary networks and dense physical environments. This manuscript proposes the [...] Read more.
Dynamic knowledge representation in curved manifolds conventionally relies on integer-order Markovian sequence encoders, intrinsically yielding exponential memory decay. This paradigm fails to model the anomalous diffusion and heavy-tailed historical dependencies inherent in complex evolutionary networks and dense physical environments. This manuscript proposes the Fractional–Temporal Lorentz Graph Convolutional Network (FTL-GCN), formalizing temporal evolution as a continuous fractional geometric flow explicitly defined on the tangent bundle of the Lorentz manifold. Analytical derivations demonstrate that the discrete Grünwald–Letnikov memory kernel establishes a non-exponential, power-law lower bound for historical state retention, preventing topological manifold collapse over extended temporal horizons. Empirical evaluations demonstrate that FTL-GCN achieves competitive forecasting accuracy against the latest 2025–2026 state-of-the-art discrete models within specific temporal windows, while uniquely mitigating predictive degradation by up to 52% in long-horizon dependency stress tests and maintaining sub-millisecond latency for physical control. The architecture is subsequently deployed within an in silico biophysical simulation for autonomous micro–nano robotic navigation in the Tumor Microenvironment (TME). By establishing a physical-mathematical structural analogy—mapping the empirical fractional viscoelasticity of the extracellular matrix to the cognitive network’s fractional derivative order—FTL-GCN sustains continuous-space navigation policies in dense anomalous environments where standard integer-order models experience mechanical slip. Full article
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