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Application of AI-Based Enabled Cyber Resilience in Sensor Networks for Infrastructure Management

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

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 2501

Special Issue Editors

Dr. Ke Yan

E-Mail Website
Guest Editor
Department of the Built Environment, National University of Singapore, Singapore 117566, Singapore
Interests: machine learning; AI; smart facilities; applied energy
Dr. Xiaodan Yan

E-Mail Website
Guest Editor
School of Cyber Science and Technology, Beihang University, Beijing 100191, China
Interests: cybersecuriy; blockchain; AI; smart city
Prof. Dr. Ji Zhang

E-Mail Website
Guest Editor
Faculty of Health, Engineering and Sciences, University of Southern Queensland, Toowoomba 4350, Australia
Interests: data mining; big data analytics; machine learning

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI)-enabled cyber resilience is becoming increasingly important for infrastructure management, including critical information management for facilities, land transport, buildings etc. While cyberattacks remain one of the key concerns behind the fast development of smart cities, new AI technologies such as smart blockchain provide new solutions to build a more cyber-resilient management plan for large-scale infrastructure facilities. The increased adoption of artificial intelligence (AI)-enabled cyber-resilience strategies addresses cybersecurity issues and creates a culture of cyber-risk awareness across infrastructure facilities.

This Special Issue intends to provide an international forum for researchers to showcase up-to-date results on AI, machine learning and cybersecurity technologies in the field of sensors. Recent progresses and future directions of AI in cyber-resilience applications will be investigated. This Special Issue also intends to bring together great efforts in computer science and various engineering fields toward finding common and cross-discipline research topics for AI-enabled cyber-resilient infrastructure management . This Special Issue will include, but not be limited to, the following topics:

  • Cybersecurity-resilient facility management strategy;
  • AI-based computer vision topics on cybersecurity-resilient infrastructure management;
  • Big data research in cybersecurity resilient infrastructure management;
  • IoT solutions for cybersecurity-resilient infrastructure management;
  • Cyber-physics systems with AI and machine learning algorithms;
  • Cybersecurity-resilient devices and instruments for smart building/city design;
  • Smart blockchain applications in smart city development;
  • Security and privacy in intelligent IoT systems;
  • Building information modeling (BIM) with cybersecurity resilience;
  • Behavior and cognitive informatics with cybersecurity resilience.
  • AI solutions on cybersecurity and trust system development;
  • Cyber-risk awareness systems development.

Dr. Ke Yan
Dr. Xiaodan Yan
Prof. Dr. Ji Zhang
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

  • smart infrastracture management
  • cybersecurity
  • resilience
  • AI
  • Big Data
  • IoT
  • cyber-physics system
  • smart city
  • building information modeling
  • trust system
  • cyber-risk awareness

Published Papers (1 paper)

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Research

13 pages, 2294 KiB  
Article
An Automatic Classification System for Environmental Sound in Smart Cities
by Dongping Zhang, Ziyin Zhong, Yuejian Xia, Zhutao Wang and Wenbo Xiong
Sensors 2023, 23(15), 6823; https://doi.org/10.3390/s23156823 - 31 Jul 2023
Cited by 2 | Viewed by 1910
Abstract
With the continuous promotion of “smart cities” worldwide, the approach to be used in combining smart cities with modern advanced technologies (Internet of Things, cloud computing, artificial intelligence) has become a hot topic. However, due to the non-stationary nature of environmental sound and [...] Read more.
With the continuous promotion of “smart cities” worldwide, the approach to be used in combining smart cities with modern advanced technologies (Internet of Things, cloud computing, artificial intelligence) has become a hot topic. However, due to the non-stationary nature of environmental sound and the interference of urban noise, it is challenging to fully extract features from the model with a single input and achieve ideal classification results, even with deep learning methods. To improve the recognition accuracy of ESC (environmental sound classification), we propose a dual-branch residual network (dual-resnet) based on feature fusion. Furthermore, in terms of data pre-processing, a loop-padding method is proposed to patch shorter data, enabling it to obtain more useful information. At the same time, in order to prevent the occurrence of overfitting, we use the time-frequency data enhancement method to expand the dataset. After uniform pre-processing of all the original audio, the dual-branch residual network automatically extracts the frequency domain features of the log-Mel spectrogram and log-spectrogram. Then, the two different audio features are fused to make the representation of the audio features more comprehensive. The experimental results show that compared with other models, the classification accuracy of the UrbanSound8k dataset has been improved to different degrees. Full article
(This article belongs to the Special Issue Application of AI-Based Enabled Cyber Resilience in Sensor Networks for Infrastructure Management)
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