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Applied Sciences
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Recent Advances in the Internet of Things (IoT): Architecture, Protocols...
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Recent Advances in the Internet of Things (IoT): Architecture, Protocols and Security, 2nd Edition

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 2024 | Viewed by 4045

Special Issue Editor

Prof. Dr. Guangquan Xu

E-Mail Website
Guest Editor
Tianjin Key Laboratory of Advanced Networking (TANK), College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
Interests: network security; wireless network; networking protocol; Internet of things (IoT); blockchain; cryptography protocol
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As an emerging technology, the Internet of Things (IoT) has become popular. The Internet of Things (IoT) is aimed at enabling the interconnection and integration of the physical world and cyber space. It represents the trend of future networking and leads the third wave of the IT industry revolution. This Special Issue aims at publishing a collection of research contributions illustrating the recent achievements in all aspects of the development, studying, and understanding of the Internet of Things. We hope to establish a collection of papers that will be of interest to scholars in the field. Contributions in the form of full papers, reviews, and communications about related topics are very welcome.

Prof. Dr. Guangquan Xu
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)
  • privacy and security
  • blockchain
  • cryptography protocol

Published Papers (5 papers)

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27 pages, 5217 KiB  
Article
A Blockchain-Driven Smart Broker for Data Quality Assurance of the Tagged Periodic IoT Data in Publisher-Subscriber Model
by Rabbia Idrees and Ananda Maiti
Appl. Sci. 2024, 14(13), 5907; https://doi.org/10.3390/app14135907 - 5 Jul 2024
Viewed by 506
Abstract
The Publisher-Subscriber model of data exchange has been a popular method for many Internet-based applications, including the Internet of Things (IoT). A traditional PS system consists of publishers, subscribers, and a broker. The publishers create new data for a registered topic, and the [...] Read more.
The Publisher-Subscriber model of data exchange has been a popular method for many Internet-based applications, including the Internet of Things (IoT). A traditional PS system consists of publishers, subscribers, and a broker. The publishers create new data for a registered topic, and the data broker relays the data to the corresponding subscribers. This paper introduces a blockchain-based smart broker for the publisher-subscriber (PS) framework for the IoT network. As IoT data comes from devices operating in various environments, it may suffer from multiple challenges, such as hardware failures, connectivity issues, and external vulnerabilities, thereby impacting data quality in terms of accuracy and timeliness. It is important to monitor this data and inform subscribers about its quality. The proposed smart broker is composed of multiple smart contracts that continuously monitor the quality of the topic data by assessing its relationship with other related topics and its drift or delay in publishing intervals. It assigns a reputation score to each topic computed based on its quality and drifts, and it passes both the original data and the reputation score as a measure of quality to the subscriber. Furthermore, the smart broker can suggest substitute topics to subscribers when the requested topic data are unavailable or of very poor quality. The evaluation shows that a smart broker efficiently monitors the reputation of the topic data, and its efficiency increases notably when the data quality is worse. As the broker is run inside the blockchain, it automatically inherits the advantages of the blockchain, and the quality scoring is indisputable based on immutable data. Full article
(This article belongs to the Special Issue Recent Advances in the Internet of Things (IoT): Architecture, Protocols and Security, 2nd Edition)
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19 pages, 1005 KiB  
Article
More Efficient and Verifiable Privacy-Preserving Aggregation Scheme for Internet of Things-Based Federated Learning
by Rongquan Shi, Lifei Wei and Lei Zhang
Appl. Sci. 2024, 14(13), 5361; https://doi.org/10.3390/app14135361 - 21 Jun 2024
Viewed by 564
Abstract
As Internet of Things (IoT) technology continues to advance at a rapid pace, smart devices have permeated daily life. Service providers are actively collecting copious numbers of user data, with the aim of refining machine learning models to elevate service quality and accuracy. [...] Read more.
As Internet of Things (IoT) technology continues to advance at a rapid pace, smart devices have permeated daily life. Service providers are actively collecting copious numbers of user data, with the aim of refining machine learning models to elevate service quality and accuracy. However, this practice has sparked apprehensions amongst users concerning the privacy and safety of their personal data. Federated learning emerges as an evolution of centralized machine learning, enabling a collective training of machine learning models by multiple users on their respective devices. Crucially, this is achieved without the direct submission of data to a central server, thereby significantly mitigating the hazards associated with privacy infringements. Since the machine learning algorithms act locally in federated learning, passing just the local model back to the central server, the users’ data remain locally. However, current research work indicates that local models also include user data privacy-related components. Moreover, current privacy-preserving secure aggregation schemes either offer insufficient accuracy or need significantly high computing resources for training. In this work, we propose an efficient and secure aggregation scheme for privacy-preserving federated learning with lower computational costs, which is suitable for those weak IoT devices since the proposed scheme is robust and fault-tolerant, allowing some of the users to dynamically exit or join the system without restarting the federated learning process or triggering abnormal termination. In addition, this scheme with the property of result verification in the situation when the servers return incorrect aggregation results, which can be verified by the users. Extensive experimental evaluations, based on real-world datasets, have substantiated the high accuracy of our proposed scheme. Moreover, in comparison to existing schemes, ours significantly reduces computational and communication costs by at least 85% and 47%, respectively. Full article
(This article belongs to the Special Issue Recent Advances in the Internet of Things (IoT): Architecture, Protocols and Security, 2nd Edition)
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28 pages, 1052 KiB  
Article
An Adaptive Security Framework for Internet of Things Networks Leveraging SDN and Machine Learning
by Ala Hamarsheh
Appl. Sci. 2024, 14(11), 4530; https://doi.org/10.3390/app14114530 - 25 May 2024
Viewed by 537
Abstract
The Internet of Things (IoT) is expanding rapidly with billions of connected devices worldwide, necessitating robust security solutions to protect these systems. This paper proposes a comprehensive and adaptive security framework called Enhanced Secure Channel Authentication using random forests and software-defined networking (SCAFFOLD), [...] Read more.
The Internet of Things (IoT) is expanding rapidly with billions of connected devices worldwide, necessitating robust security solutions to protect these systems. This paper proposes a comprehensive and adaptive security framework called Enhanced Secure Channel Authentication using random forests and software-defined networking (SCAFFOLD), tailored for IoT environments. The framework establishes secure communication channels between IoT nodes using software-defined networking (SDN) and machine learning techniques. The key components include encrypted channels using session keys, continuous traffic monitoring by the SDN controller, ensemble machine-learning for attack detection, precision mitigation via SDN reconfiguration, and periodic reauthentication for freshness. A mathematical model formally defines the protocol. Performance evaluations via extensive simulations demonstrate Enhanced SCAFFOLD’s ability to reliably detect and rapidly mitigate various attacks with minimal latency and energy consumption overheads across diverse IoT network scenarios and traffic patterns. The multidimensional approach combining encryption, intelligent threat detection, surgical response, and incremental hardening provides defense-in-depth to safeguard availability, integrity, and privacy within modern IoT systems while preserving quality of service. Full article
(This article belongs to the Special Issue Recent Advances in the Internet of Things (IoT): Architecture, Protocols and Security, 2nd Edition)
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14 pages, 1684 KiB  
Article
Mean-Field Stackelberg Game-Based Security Defense and Resource Optimization in Edge Computing
by Li Miao, Shuai Li, Xiangjuan Wu and Bingjie Liu
Appl. Sci. 2024, 14(9), 3538; https://doi.org/10.3390/app14093538 - 23 Apr 2024
Cited by 1 | Viewed by 504
Abstract
Edge computing brings computation and storage resources to the edge of the mobile network to solve the problems of low latency and high real-time demand. However, edge computing is more vulnerable to malicious attacks due to its open and dynamic environments. In this [...] Read more.
Edge computing brings computation and storage resources to the edge of the mobile network to solve the problems of low latency and high real-time demand. However, edge computing is more vulnerable to malicious attacks due to its open and dynamic environments. In this article, we investigate security defense strategies in edge computing systems, focusing on scenarios with one attacker and multiple defenders to determine optimal defense strategies with minimal resource allocation. Firstly, we formulate the interactions between the defenders and the attackers as the mean-field Stackelberg game model, where the state and the objective functions of the defenders are coupled through the mean-field term, and are strongly influenced by the strategy of the attacker. Then, we analyze the local optimal strategies of the defenders given an arbitrary strategy of the attackers. We demonstrate the Nash equilibrium and the mean-field equilibrium for both the defenders and the attackers. Finally, simulation analysis will illustrate the dynamic evolution of the defense strategy of the defenders and the trajectory of the attackers based on the proposed Stackelberg game model. Full article
(This article belongs to the Special Issue Recent Advances in the Internet of Things (IoT): Architecture, Protocols and Security, 2nd Edition)
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26 pages, 6605 KiB  
Article
Design and Evaluation of Wireless DYU Air Box for Environment-Monitoring IoT System on Da-Yeh University Campus
by Lun-Min Shih, Huan-Liang Tsai and Cheng-Yu Tsai
Appl. Sci. 2024, 14(5), 2201; https://doi.org/10.3390/app14052201 - 6 Mar 2024
Viewed by 975
Abstract
This paper presents an original wireless DYU Air Box of an environment-monitoring IoT (EMIoT) system on a campus to offer information on environmental conditions through the public ThingSpeak IoT platform for stakeholders including all the students and employees on the Da-Yeh University (DYU) [...] Read more.
This paper presents an original wireless DYU Air Box of an environment-monitoring IoT (EMIoT) system on a campus to offer information on environmental conditions through the public ThingSpeak IoT platform for stakeholders including all the students and employees on the Da-Yeh University (DYU) campus in Taiwan. Firstly, the proposed wireless heterogeneous multi-sensor module aggregates BME680, SCD30, PMS7003, and BH1750 sensors with a TTGO ESP32 Wi-Fi device based on the I2C and UART interface standards of series communication. Through the DYU-802.1X Wi-Fi network with the WPA2 Enterprise security directly, the wireless multi-sensor monitoring module further forwards the observation data of environmental conditions on campus via the DYU-802.1X Wi-Fi network to the public ThingSpeak IoT platform, which is a cloud service platform to aggregate, visualize, and analyze live sensing data of air quality index (AQI), concentrations of PM1.0/2.5 and CO2, brightness, ambient temperature, and relative humidity (RH). The results illustrate the proposed DYU Air Box for monitoring the indoor environmental conditions on campus and validate them with sufficient accuracy and confidence with commercialized measurement instruments. In this work, the wireless smart environment-monitoring IoT system features monitoring and automatic alarm functions for monitoring AQI, CO2, and PM concentrations, as well as ambient illumination, temperature, and RH parameters and collaboration and interoperability through the Enterprise Intranet. All the organizational stakeholders interested in the environmental conditions of the DYU campus can openly access the information according to their interests. In the upcoming future, the information of the environmental conditions in the DYU campus will be developed to be simultaneously accessed by all the stakeholders through both the public ThingSpeak IoT platform and the private EMIoT system. Full article
(This article belongs to the Special Issue Recent Advances in the Internet of Things (IoT): Architecture, Protocols and Security, 2nd Edition)
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