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Mathematics
Special Issues
Intelligent Edge Computing: Algorithms, Security, and Artificial Intelligence-Driven Applications
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Intelligent Edge Computing: Algorithms, Security, and Artificial Intelligence-Driven Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 1492

Special Issue Editors

Dr. Tiehua Zhang

E-Mail Website
Guest Editor
School of Computer Science and Technology, Tongji University, Shanghai, China
Interests: edge intelligence; artificial intelligence
Dr. Feifei Xu

E-Mail Website
Guest Editor
School of Computer and Information Engineering, Shanghai University of Electric Power, Shanghai, China
Interests: artificial intelligence; multimodal; large language models
Dr. Zhishu Shen

E-Mail Website
Guest Editor
School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, China
Interests: Iot; edge intelligence; space-air-ground integrated network
Dr. Qi Zhong

E-Mail Website
Guest Editor
Faculty of Data Science, City University of Macau, Macau, China
Interests: copyright protection of machine learning models; machine learning backdoors; adversarial machine learning; data privacy protection in cloud and Internet of Things

Special Issue Information

Dear Colleagues,

The exponential growth of IoT devices and latency-sensitive applications demands a paradigm shift beyond traditional cloud computing. Edge intelligence (EI) emerges as the critical solution, bringing computation and data storage, as well as AI capabilities, closer to data sources, thus facilitating responsive intelligent service provisions to the end users. This Special Issue seeks cutting-edge research at the powerful intersection of algorithms, security, and artificial intelligence (AI) which drives next-generation cloud-edge consortium.

We invite original research contributions addressing the unique challenges and opportunities within EI. Topics may include, but are not limited to, the following:

  1. Resource-efficient AI/ML models for edge deployment, distributed learning (federated, split), lightweight inference, task scheduling, offloading, and optimization techniques for constrained edge environments.
  2. Robust security architectures, privacy-preserving techniques, intrusion detection/prevention for edge networks, secure firmware updates, trust management, and resilience against novel edge-specific threats.
  3. Innovative use cases leveraging intelligent edge AI in smart cities, industrial IoT (IIoT), autonomous vehicles, healthcare, AR/VR, precision agriculture, and real-time analytics etc., demonstrating tangible benefits and novel system designs.

Dr. Tiehua Zhang
Dr. Feifei Xu
Dr. Zhishu Shen
Dr. Qi Zhong
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 250 words) can be sent to the Editorial Office for assessment.

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. Mathematics 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

  • edge computing
  • AI-driven edge applications
  • Internet of Things
  • federated/distributed learning
  • cloud-edge service orchestration
  • edge-centric LLMs

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Published Papers (1 paper)

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Research

20 pages, 1950 KB  
Article
Anomalous Sound Detection by Fusing Spectral Enhancement and Frequency-Gated Attention
by Zhongqin Bi, Jun Jiang, Weina Zhang and Meijing Shan
Mathematics 2026, 14(3), 530; https://doi.org/10.3390/math14030530 - 2 Feb 2026
Viewed by 854
Abstract
Unsupervised anomalous sound detection aims to learn acoustic features solely from the operational sounds of normal equipment and identify potential anomalies based on these features. Recent self-supervised classification frameworks based on machine ID metadata have achieved promising results, but they still face two [...] Read more.
Unsupervised anomalous sound detection aims to learn acoustic features solely from the operational sounds of normal equipment and identify potential anomalies based on these features. Recent self-supervised classification frameworks based on machine ID metadata have achieved promising results, but they still face two challenges in industrial acoustic scenarios: Log-Mel spectrograms tend to weaken high-frequency details, leading to insufficient spectral characterization, and when normal sounds from different machine IDs are highly similar, classification constraints alone struggle to form clear intra-class structures and inter-class boundaries, resulting in false positives. To address these issues, this paper proposes FGASpecNet, an anomaly detection model integrating spectral enhancement and frequency-gated attention. For feature modeling, a spectral enhancement branch is designed to explicitly supplement spectral details, while a frequency-gated attention mechanism highlights key frequency bands and temporal intervals conditioned on temporal context. Regarding loss design, a joint training strategy combining classification loss and metric learning loss is adopted. Multi-center prototypes enhance intra-class compactness and inter-class separability, improving detection performance in scenarios with similar machine IDs. Experimental results on the DCASE 2020 Challenge Task 2 for anomalous sound detection demonstrate that FGASpecNet achieves 95.04% average AUC and 89.68% pAUC, validating the effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue Intelligent Edge Computing: Algorithms, Security, and Artificial Intelligence-Driven Applications)
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