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Applied Sciences
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Internet of Things: Recent Advances and Applications
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Internet of Things: Recent Advances and Applications

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 September 2025 | Viewed by 8180

Special Issue Editors

Dr. Gaolei Li

E-Mail Website
Guest Editor
Institute of Cyber Science and Technology, Shanghai Jiao Tong University, Shanghai, China
Interests: adversarial machine learning; data poisoning and defense; network security; Internet of Things
Dr. Xi Lin

E-Mail Website
Guest Editor
Institute of Cyber Science and Technology, Shanghai Jiao Tong University, Shanghai, China
Interests: data security; blockchain; privacy computing; intelligent Internet of Things
Prof. Dr. Jianhua Li

E-Mail Website
Guest Editor
Institute of Cyber Science and Technology, Shanghai Jiao Tong University, Shanghai, China
Interests: cyberspace security; information security; e-government collaborative work and secure data exchange theory and application technology; content security management theory and application

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) could provide ubiquitous low-latency connectivity for various new applications such as the metaverse, industry 5.0, and space–ground integration via large-scale models, edge AI, blockchain technology, coded computing, etc.

 With the emergence of new technologies, the field of IoT has witnessed significant development in recent years, especially in terms of data processing, context awareness, coverage optimization, energy consumption control, and security protection.

This Special Issue aims to collate high-quality and original works regarding the recent advances and applications of Internet of Things technology. Potential topics include (but are not limited to) the following:

  • IoT massive data sharing and processing via edge AI;
  • IoT sensing and networking via semantic communication;
  • IoT energy saving via advanced low-power designs;
  • IoT coverage optimization via 5G/6G networking;
  • IoT interoperability via large-scale models;
  • IoT malware detection via deep learning;
  • IoT vulnerability identification;
  • IoT data poisoning and defense;
  • IoT security and privacy via blockchain technology;
  • Underwater IoT applications;
  • IoT applications in space;
  • Experimental IoT prototyping and testbeds.

Dr. Gaolei Li
Dr. Xi Lin
Prof. Dr. Jianhua Li
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. 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
  • large-scale model
  • blockchain
  • edge AI
  • security and privacy

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Published Papers (3 papers)

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Research

24 pages, 680 KiB  
Article
Ambient Backscatter- and Simultaneous Wireless Information and Power Transfer-Enabled Switch for Indoor Internet of Things Systems
by Vishalya P. Sooriarachchi, Tharindu D. Ponnimbaduge Perera and Dushantha Nalin K. Jayakody
Appl. Sci. 2025, 15(1), 478; https://doi.org/10.3390/app15010478 - 6 Jan 2025
Viewed by 864
Abstract
Indoor Internet of Things (IoT) is considered as a crucial component of Industry 4.0, enabling devices and machine to communicate and share sensed data leading to increased efficiency, productivity, and automation. Increased energy efficiency is a significant focus within Industry 4.0, as it [...] Read more.
Indoor Internet of Things (IoT) is considered as a crucial component of Industry 4.0, enabling devices and machine to communicate and share sensed data leading to increased efficiency, productivity, and automation. Increased energy efficiency is a significant focus within Industry 4.0, as it offers numerous benefits. To support this focus, we developed a hybrid switching mechanism to switch between energy harvesting techniques, ambient backscattering and Simultaneous Wireless Information and Power Transfer (SWIPT), which can be utilized within cooperative communications. To implement the proposed switching mechanism, we consider an indoor warehouse environment, where the moving sensor node transmits sensed data to the fixed relay located on the roof, which is then transmitted to an IoT gateway. The relay is equipped with the proposed switch to energize its communication capabilities while maintaining the expected quality of service at the IoT gateway. Simulation results illustrate the improved energy efficiency within the indoor communication setup while maintaining QoS at varying signal-to-noise (SNR) conditions. Full article
(This article belongs to the Special Issue Internet of Things: Recent Advances and Applications)
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20 pages, 2957 KiB  
Article
Optimization of Electric Vehicle Charging Control in a Demand-Side Management Context: A Model Predictive Control Approach
by Victor Fernandez and Virgilio Pérez
Appl. Sci. 2024, 14(19), 8736; https://doi.org/10.3390/app14198736 - 27 Sep 2024
Cited by 5 | Viewed by 3763
Abstract
In this paper, we propose a novel demand-side management (DSM) system designed to optimize electric vehicle (EV) charging at public stations using model predictive control (MPC). The system adjusts to real-time grid conditions, electricity prices, and user preferences, providing a dynamic approach to [...] Read more.
In this paper, we propose a novel demand-side management (DSM) system designed to optimize electric vehicle (EV) charging at public stations using model predictive control (MPC). The system adjusts to real-time grid conditions, electricity prices, and user preferences, providing a dynamic approach to energy distribution in smart city infrastructures. The key focus of the study is on reducing peak loads and enhancing grid stability, while minimizing charging costs for end users. Simulations were conducted under various scenarios, demonstrating the effectiveness of the proposed system in mitigating peak demand and optimizing energy use. Additionally, the system’s flexibility enables the adjustment of charging schedules to meet both grid requirements and user needs, making it a scalable solution for smart city development. However, current limitations include the assumption of uniform tariffs and the absence of renewable energy considerations, both of which are critical in real-world applications. Future research will focus on addressing these issues, improving scalability, and integrating renewable energy sources. The proposed framework represents a significant step towards efficient energy management in urban settings, contributing to both cost savings and environmental sustainability. Full article
(This article belongs to the Special Issue Internet of Things: Recent Advances and Applications)
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21 pages, 1036 KiB  
Article
Blockchain and Access Control Encryption-Empowered IoT Knowledge Sharing for Cloud-Edge Orchestrated Personalized Privacy-Preserving Federated Learning
by Jing Wang and Jianhua Li
Appl. Sci. 2024, 14(5), 1743; https://doi.org/10.3390/app14051743 - 21 Feb 2024
Cited by 6 | Viewed by 2653
Abstract
Federated learning (FL) is emerging as a powerful paradigm for distributed data mining in the context of Internet of Things (IoT) big data. It addresses privacy concerns associated with data outsourcing by enabling local data training and knowledge (i.e., model) sharing. However, simplistic [...] Read more.
Federated learning (FL) is emerging as a powerful paradigm for distributed data mining in the context of Internet of Things (IoT) big data. It addresses privacy concerns associated with data outsourcing by enabling local data training and knowledge (i.e., model) sharing. However, simplistic local knowledge sharing can inadvertently expose user privacy to advanced attacks, such as model inversion or gradient leakage. Furthermore, achieving fine-grained and personalized privacy protection for IoT users remains a challenge. In this paper, we propose a novel solution called hierarchical blockchain-empowered cloud-edge orchestrated federated learning (HBCE-FL) to address these challenges. HBCE-FL is designed to provide secure, intelligent, and distributed data analysis for IoT users. To tackle FL’s privacy issues, we develop a multi-level access control encryption and blockchain-based approach for sharing IoT knowledge within the HBCE-FL framework. Our approach classifies IoT users into different levels based on their individual privacy requirements, enabling fine-grained privacy protection. The blockchain is employed for identity authentication, key management, and message sanitization. For scenarios involving IoT users with non-IID data, we integrate federated multi-task learning into HBCE-FL to ensure fairness, robustness, and privacy. Finally, we conduct experiments using classic MNIST and CIFAR10 datasets to validate our approach. The experimental results illustrate that HBCE-FL effectively achieves personalized privacy-preserving FL without losing IoT data availability. Regardless of whether IoT data are homogeneous or heterogeneous, our approach enhances model accuracy and convergence rates by enabling secure IoT knowledge access and sharing for IoT users. Full article
(This article belongs to the Special Issue Internet of Things: Recent Advances and Applications)
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