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Sensor-Based Computational Intelligence and Security Technologies, and Their Applications
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Sensor-Based Computational Intelligence and Security Technologies, and Their Applications

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 843

Special Issue Editors

Prof. Dr. Yiu-ming Cheung

E-Mail Website
Guest Editor
Department of Computer Science, Hong Kong Baptist University, Hong Kong, China
Interests: machine learning; visual computing; data science; pattern recognition; multi-objective optimization; information security
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yuping Wang

E-Mail Website
Guest Editor
School of Computer Science and Technology, Xidian University, Xi'an 710071, China
Interests: evolutionary computing; data mining; cyber security
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xin Liu

E-Mail Website
Guest Editor
College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China
Interests: computer vision; data mining; deep learning
Prof. Dr. Hai-Lin Liu

E-Mail Website
Guest Editor
School of Mathematics and Statistics, Guangdong University of Technology, Guangzhou 510520, China
Interests: evolutionary computation; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advanced sensors and sensing technologies have facilitated the fast development of abundant innovative applications, including industrial automation, security monitoring, consumer electronics, and so forth. Specifically, computational intelligence and security technologies are the fundamental components in the development of these advanced sensing technologies. Computational intelligence, with its robust set of tools, including machine learning, deep learning, and evolutionary algorithms, is emerging as a key enabler for developing intelligent sensor systems. Security technology, with its significant advancements through the integration of security defense and privacy-preserving deployment, has led to notable improvements in information protection. Currently, we are experiencing new innovative methodologies emerging from everywhere in the world, as well as an adaptability to unexpected conditions, which increases the usefulness of these advanced methodologies for an expanding array of real-world sensor applications.

This Special Issue invites researchers to present their latest research findings, address existing challenges in sensing technologies, and explore future directions in computational intelligence and security technologies. Topics of interest include the following: 

  • Sensors and sensing technologies;
  • Computational intelligence;
  • Security technologies;
  • Machine learning and data mining;
  • Cybersecurity applications;
  • Machine learning for anomaly detection;
  • Privacy-preserving techniques;
  • AI-based security protocols;
  • Smart cities and security;
  • Autonomous systems security;
  • Data-driven security solutions;
  • Deep learning for intelligence.

Prof. Dr. Yiu-ming Cheung
Prof. Dr. Yuping Wang
Prof. Dr. Xin Liu
Prof. Dr. Hai-Lin Liu
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

  • sensor technologies
  • computational intelligence
  • security technologies
  • machine learning
  • privacy-preserving techniques

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

24 pages, 3232 KiB  
Article
An Anomaly Node Detection Method for Wireless Sensor Networks Based on Deep Metric Learning with Fusion of Spatial–Temporal Features
by Ziheng Wang, Miao Ye, Jin Cheng, Cheng Zhu and Yong Wang
Sensors 2025, 25(10), 3033; https://doi.org/10.3390/s25103033 - 12 May 2025
Viewed by 256
Abstract
Wireless sensor networks (WSNs) use distributed nodes for tasks such as environmental monitoring and surveillance. The existing anomaly detection methods fail to fully capture correlations in multi-node, multi-modal time series data, limiting their effectiveness. Additionally, they struggle with small sample scenarios because they [...] Read more.
Wireless sensor networks (WSNs) use distributed nodes for tasks such as environmental monitoring and surveillance. The existing anomaly detection methods fail to fully capture correlations in multi-node, multi-modal time series data, limiting their effectiveness. Additionally, they struggle with small sample scenarios because they do not effectively map features to classes. To address these challenges, this paper presents an anomaly detection approach that integrates deep learning with metric learning. A framework incorporating a graph attention network (GAT) and a Transformer is developed to capture spatial and temporal features. A novel distance measurement module improves similarity learning by considering both intra-class and inter-class relationships. Joint metric-classification training improves model accuracy and generalization. Experiments conducted on public datasets demonstrate that the proposed approach achieves an F1 score of 0.89, outperforming the existing approaches by 7%. Full article
(This article belongs to the Special Issue Sensor-Based Computational Intelligence and Security Technologies, and Their Applications)
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18 pages, 4969 KiB  
Article
Temporal Decay Loss for Adaptive Log Anomaly Detection in Cloud Environments
by Lelisa Adeba Jilcha, Deuk-Hun Kim and Jin Kwak
Sensors 2025, 25(9), 2649; https://doi.org/10.3390/s25092649 - 22 Apr 2025
Viewed by 304
Abstract
Log anomaly detection in cloud computing environments is essential for maintaining system reliability and security. While sequence modeling architectures such as LSTMs and Transformers have been widely employed to capture temporal dependencies in log messages, their effectiveness deteriorates in zero-shot transfer scenarios due [...] Read more.
Log anomaly detection in cloud computing environments is essential for maintaining system reliability and security. While sequence modeling architectures such as LSTMs and Transformers have been widely employed to capture temporal dependencies in log messages, their effectiveness deteriorates in zero-shot transfer scenarios due to distributional shifts in log structures, terminology, and event frequencies, as well as minimal token overlap across datasets. To address these challenges, we propose an effective detection approach integrating a domain-specific pre-trained language model (PLM) fine-tuned on cybersecurity-adjacent data with a novel loss function, Loss with Decaying Factor (LDF). LDF introduces an exponential time decay mechanism into the training objective, ensuring a dynamic balance between historical context and real-time relevance. Unlike traditional sequence models that often overemphasize outdated information and impose high computational overhead, LDF constrains the training process by dynamically weighing log messages based on their temporal proximity, thereby aligning with the rapidly evolving nature of cloud computing environments. Additionally, the domain-specific PLM mitigates semantic discrepancies by improving the representation of log data across heterogeneous datasets. Extensive empirical evaluations on two supercomputing log datasets demonstrate that this approach substantially enhances cross-dataset anomaly detection performance. The main contributions of this study include: (1) the introduction of a Loss with Decaying Factor (LDF) to dynamically balance historical context with real-time relevance; and (2) the integration of a domain-specific PLM for enhancing generalization in zero-shot log anomaly detection across heterogeneous cloud environments. Full article
(This article belongs to the Special Issue Sensor-Based Computational Intelligence and Security Technologies, and Their Applications)
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