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Applied Sciences
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IoT and Edge Computing for Smart Infrastructure and Cybersecurity
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IoT and Edge Computing for Smart Infrastructure and Cybersecurity

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

Deadline for manuscript submissions: 20 July 2025 | Viewed by 894

Special Issue Editor

Prof. Dr. Kai Peng

E-Mail Website
Guest Editor
Hubei Key Laboratory of Smart Internet Technology, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: cloud computing; edge computing; distributed artificial intelligence; artificial intelligence security

Special Issue Information

Dear Colleagues,

The integration of the Internet of Things (IoT) and edge computing is transforming the development of smart infrastructure and enhancing cybersecurity capabilities. IoT devices, combined with edge computing, enable the processing of large volumes of data closer to the data source, resulting in faster response times, reduced latency, and improved security, which are essential for smart infrastructure applications. These advancements offer novel opportunities in diverse areas, such as smart cities, intelligent transportation, energy management, and industrial automation, while simultaneously addressing key challenges in data privacy and cybersecurity.

This Special Issue aims to highlight recent developments, innovative research, and industrial applications of the IoT and edge computing in the context of smart infrastructure and cybersecurity. We encourage submissions from both academic researchers and industry practitioners on topics related but not limited to the following:

  • Edge computing for real-time data processing in smart infrastructure;
  • IoT-based solutions for intelligent transportation systems;
  • Cybersecurity strategies for edge and IoT environments;
  • Privacy-preserving techniques in IoT and edge computing;
  • Applications of IoT in industrial automation and energy management;
  • Network architecture and protocols for secure edge computing;
  • AI-driven edge analytics for smart infrastructure;
  • Integration of IoT with blockchain for enhanced security.

We look forward to your valuable contributions to this Special Issue.

Prof. Dr. Kai Peng
Guest Editor

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.

Keywords

  • Internet of Things (IoT)
  • edge computing
  • smart infrastructure
  • cybersecurity
  • data privacy
  • intelligent transportation
  • industrial automation
  • real-time data processing
  • AI-driven analytics
  • blockchain security

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Published Papers (3 papers)

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Research

31 pages, 2276 KiB  
Article
Dynamic UAV Inspection Boosted by Vehicle Collaboration Under Harsh Conditions in the IoT Realm
by Dai Hou, Zhiheng Yao, Bo Jin, Xingwei Cai, Huan Xu, Jiaxiang Xu and Tianping Deng
Appl. Sci. 2025, 15(9), 4671; https://doi.org/10.3390/app15094671 - 23 Apr 2025
Abstract
With the widespread adoption of the Internet of Things (IoT), UAV–vehicle collaborative inspection systems are crucial for large-scale, IoT-enabled monitoring. Empowered by the IoT, these systems optimize resource allocation and boost the efficiency of IoT-based applications. Nevertheless, variable vehicle and UAV speeds due [...] Read more.
With the widespread adoption of the Internet of Things (IoT), UAV–vehicle collaborative inspection systems are crucial for large-scale, IoT-enabled monitoring. Empowered by the IoT, these systems optimize resource allocation and boost the efficiency of IoT-based applications. Nevertheless, variable vehicle and UAV speeds due to wind and precipitation complicate path planning and task scheduling in the IoT-integrated setup. To solve this, this study offers an adaptive solution for dynamic, complex-weather scenarios within the IoT framework. A dynamic task-processing model was developed first, using real-time IoT sensor data for better decisions. Then, the KGTSA optimization algorithm was designed. It combines K-means clustering, HGA, and TS, considering UAV and vehicle speed variations in complex weather and making full use of IoT-device data. K-means generates an initial solution, HGA refines it, and TS fine-tunes UAV routes and task assignments. The simulation results show that KGTSA significantly cuts data collection time while maintaining flexibility. It efficiently manages speed and path uncertainties in complex weather, optimizing task efficiency without weather forecasts. Compared to traditional algorithms, KGTSA shortens data collection time and adapts better to dynamic IoT environments for real-world efficiency. Full article
(This article belongs to the Special Issue IoT and Edge Computing for Smart Infrastructure and Cybersecurity)
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23 pages, 7619 KiB  
Article
A Blockchain-Based Collaborative Storage Scheme for Roadside Unit Clusters in Social Internet of Vehicles
by Dai Hou, Lan Wei, Lei Zheng, Geng Wu, Jiaxing Hu, Chenxi Dong, Xinru Li and Kai Peng
Appl. Sci. 2025, 15(8), 4573; https://doi.org/10.3390/app15084573 - 21 Apr 2025
Abstract
With the gradual application of blockchain technology in the domain of Social Internet of Vehicles (SIoV), the increasing volume of blockchain data has imposed significant storage pressure on roadside units (RSUs). Collaborative storage schemes, which organize RSUs into clusters to jointly store content [...] Read more.
With the gradual application of blockchain technology in the domain of Social Internet of Vehicles (SIoV), the increasing volume of blockchain data has imposed significant storage pressure on roadside units (RSUs). Collaborative storage schemes, which organize RSUs into clusters to jointly store content for vehicles, have been explored. However, existing collaborative storage solutions in IoV primarily focus on caching content and are not well-suited to the deployment constraints of blockchain networks. Building on blockchain’s decentralized characteristics and data integrity mechanisms, this paper proposes a collaborative storage scheme that reduces RSU storage loads while sustaining distributed ledger operations in SIoV. Specifically, the RSU Access Preference-based Spectral Clustering Algorithm (RAPSCA) is proposed to address RSU clustering by analyzing both the RSUs’ access preferences for blockchain data and their resource availability. Subsequently, the Vehicle Service Priority-based Greedy Block Allocation Algorithm (VSPGBAA) is devised for intra-cluster storage allocation, which considers vehicles’ dwell times and block access probabilities to reduce overall access costs. Experimental results indicate that, compared to baseline algorithms, the proposed method achieves a 27.7% reduction in cost and a 3.5-fold decrease in execution time, thereby demonstrating the feasibility of collaborative storage optimization in blockchain-enabled SIoV. Full article
(This article belongs to the Special Issue IoT and Edge Computing for Smart Infrastructure and Cybersecurity)
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23 pages, 6244 KiB  
Article
Synchronous Remote Calibration for Electricity Meters: Application and Optimization
by Zhiyong Zha, Hanfang Ge, Chengcheng Zou, Fei Long, Xingfeng He, Geng Wu, Chenxi Dong, Tianping Deng and Jiaxiang Xu
Appl. Sci. 2025, 15(3), 1259; https://doi.org/10.3390/app15031259 - 26 Jan 2025
Viewed by 541
Abstract
Remote calibration is an advanced methodology that leverages electricity meters, intelligent detection, and computing technologies to enhance calibration efficiency and precision significantly. However, current research predominantly focuses on isolated calibration architectures tailored to single-laboratory environments. In contrast, distributed remote calibration systems that integrate [...] Read more.
Remote calibration is an advanced methodology that leverages electricity meters, intelligent detection, and computing technologies to enhance calibration efficiency and precision significantly. However, current research predominantly focuses on isolated calibration architectures tailored to single-laboratory environments. In contrast, distributed remote calibration systems that integrate multiple nodes remain in their early developmental stages, despite their considerable potential for improving scalability and operational efficiency. The purpose of this paper is to propose a multi-point collaborative distributed remote calibration model that improves scalability and operational efficiency for remote sensing devices. It addresses the challenge of resource allocation for synchronous calibration across distributed nodes by introducing a hybrid genetic algorithm that optimises scheduling and resource management. Experimental results reveal that the proposed algorithm surpasses other comparable methods in its category, highlighting its capability to improve resource efficiency in distributed remote calibration systems. Additionally, the hybrid genetic algorithm offers profound insights and effective solutions to the intricate challenges of task scheduling in dual-container synchronisation, enhancing both scheduling performance and system dependability. Full article
(This article belongs to the Special Issue IoT and Edge Computing for Smart Infrastructure and Cybersecurity)
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