Industrial Internet of Things (IIoT): New Directions, Challenges and Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: 20 June 2024 | Viewed by 5814

Special Issue Editors

Department of Internet of Things Engineering, Hohai University, Changzhou 213022 , China
Interests: Internet of Things; machine learning and artificial intelligence; smart ocean; industrial internet; security and privacy
Special Issues, Collections and Topics in MDPI journals
School of Software, Northeastern University, Shenyang 110819, China
Interests: software-defined networking; Industrial Internet of Things; multi-agent network; artificial intelligence

Special Issue Information

Dear Colleagues,

The Industrial Internet of Things (IIoT) is an important part of Industry 4.0. The Industrial Internet of Things continuously integrates various acquisition or control sensors or controllers with sensing and monitoring capabilities, as well as ubiquitous technology, mobile communication, intelligent analysis, and other technologies into all aspects of the industrial production process, thereby greatly improving manufacturing efficiency and product quality, reducing product cost and resource consumption, and finally realizing the promotion of traditional industry to a new stage of intelligence. From the application form, the application of the Industrial Internet of Things has the characteristics of real-time, automation, embedded (software), security, and information interoperability. However, numerous relevant unsolved theoretical and technological problems await further research. This Special Issue aims to address the aforementioned questions by inviting scholarly contributions covering recent advances in the Industrial Internet of Things and its applications. We welcome original research articles covering new applications, challenges, and developments in IIoT, as well as IIoT application papers with novel ideas.

Prof. Dr. Guangjie Han
Dr. Chuan Lin
Guest Editors

Manuscript Submission Information

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Keywords

  • Internet of Things (Iot)
  • Industry 4.0
  • artificial intelligence (AI)
  • neural networks
  • cyber-physical systems
  • real-time embedded systems
  • smart sensors
  • sensor networks and signal processing
  • industrial IoT applications in specific domains of manufacture, the supply chain, and healthcare

Published Papers (5 papers)

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Research

17 pages, 2152 KiB  
Article
Research on Intrusion Detection Based on an Enhanced Random Forest Algorithm
Appl. Sci. 2024, 14(2), 714; https://doi.org/10.3390/app14020714 - 15 Jan 2024
Viewed by 642
Abstract
To address the challenges posed by high data dimensionality and class imbalance during intrusion detection, which result in increased computational complexity, resource consumption, and reduced classification accuracy, this paper presents an intrusion-detection algorithm based on an improved Random Forest approach. The algorithm employs [...] Read more.
To address the challenges posed by high data dimensionality and class imbalance during intrusion detection, which result in increased computational complexity, resource consumption, and reduced classification accuracy, this paper presents an intrusion-detection algorithm based on an improved Random Forest approach. The algorithm employs the Bald Eagle Search (BES) optimization technique to fine-tune the Kernel Principal Component Analysis (KPCA) algorithm, enabling optimized dimensionality reduction. The processed data are then fed into a cost-sensitive Random Forest classifier for training, with subsequent model validation conducted on the reduced-dimension data. Experimental results demonstrate that compared to traditional Random Forest algorithms, the proposed method reduces the training time by 11.32 s and achieves a 5.59% increase in classification accuracy, an 11.7% improvement in specificity, and a 0.0558 increase in the G-mean value. These findings underscore the promising application potential and performance of this approach in the field of network intrusion detection. Full article
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19 pages, 949 KiB  
Article
Structural-Missing Tensor Completion for Robust DOA Estimation with Sensor Failure
Appl. Sci. 2023, 13(23), 12740; https://doi.org/10.3390/app132312740 - 28 Nov 2023
Viewed by 568
Abstract
Array sensor failure poses a serious challenge to robust direction-of-arrival (DOA) estimation in complicated environments. Although existing matrix completion methods can successfully recover the damaged signals of an impaired sensor array, they cannot preserve the multi-way signal characteristics as the dimension of arrays [...] Read more.
Array sensor failure poses a serious challenge to robust direction-of-arrival (DOA) estimation in complicated environments. Although existing matrix completion methods can successfully recover the damaged signals of an impaired sensor array, they cannot preserve the multi-way signal characteristics as the dimension of arrays expands. In this paper, we propose a structural-missing tensor completion algorithm for robust DOA estimation with uniform rectangular array (URA), which exhibits a high robustness to non-ideal sensor failure conditions. Specifically, the signals received at the impaired URA are represented as a three-dimensional incomplete tensor, which contains whole fibers or slices of missing elements. Due to this structural-missing pattern, the conventional low-rank tensor completion becomes ineffective. To resolve this issue, a spatio-temporal dimension augmentation method is developed to transform the structural-missing tensor signal into a six-dimensional Hankel tensor with dispersed missing elements. The augmented Hankel tensor can then be completed with a low-rank regularization by solving a Hankel tensor nuclear norm minimization problem. As such, the inverse Hankelization on the completed Hankel tensor recovers the tensor signal of an unimpaired URA. Accordingly, a completed covariance tensor can be derived and decomposed for robust DOA estimation. Simulation results verify the effectiveness of the proposed algorithm. Full article
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19 pages, 2714 KiB  
Article
A Blockchain-Based Privacy-Preserving and Fair Data Transaction Model in IoT
Appl. Sci. 2023, 13(22), 12389; https://doi.org/10.3390/app132212389 - 16 Nov 2023
Viewed by 629
Abstract
The rapid development of the Internet of Things (IoT) has resulted in vast amounts of widely distributed data. Sharing these data can spur innovative advancements and enhance service quality. However, conventional data-sharing methods often involve third-party intermediaries, posing risks of single-point failures and [...] Read more.
The rapid development of the Internet of Things (IoT) has resulted in vast amounts of widely distributed data. Sharing these data can spur innovative advancements and enhance service quality. However, conventional data-sharing methods often involve third-party intermediaries, posing risks of single-point failures and privacy leaks. Moreover, these traditional sharing methods lack a secure transaction model to compensate for data sharing, which makes ensuring fair payment between data consumers and providers challenging. Blockchain, as a decentralized, secure, and trustworthy distributed ledger, offers a novel solution for data sharing. Nevertheless, since all nodes on the blockchain can access on-chain data, data privacy is inadequately protected, and traditional privacy-preserving methods like anonymization and generalization are ineffective against attackers with background knowledge. To address these issues, this paper proposes a decentralized, privacy-preserving, and fair data transaction model based on blockchain technology. We designed an adaptive local differential privacy algorithm, MDLDP, to protect the privacy of transaction data. Concurrently, verifiable encrypted signatures are employed to address the issue of fair payment during the data transaction process. This model proposes a committee structure to replace the individual arbitrator commonly seen in traditional verifiable encrypted signatures, thereby reducing potential collusion between dishonest traders and the arbitrator. The arbitration committee leverages threshold signature techniques to manage arbitration private keys. A full arbitration private key can only be collaboratively constructed by any arbitrary t members, ensuring the key’s security. Theoretical analyses and experimental results reveal that, in comparison to existing approaches, our model delivers enhanced transactional security. Moreover, while guaranteeing data availability, MDLDP affords elevated privacy protection. Full article
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21 pages, 5398 KiB  
Article
A Hierarchical Federated Learning Algorithm Based on Time Aggregation in Edge Computing Environment
Appl. Sci. 2023, 13(9), 5821; https://doi.org/10.3390/app13095821 - 08 May 2023
Cited by 1 | Viewed by 1903
Abstract
Federated learning is currently a popular distributed machine learning solution that often experiences cumbersome communication processes and challenging model convergence in practical edge deployments due to the training nature of its model information interactions. The paper proposes a hierarchical federated learning algorithm called [...] Read more.
Federated learning is currently a popular distributed machine learning solution that often experiences cumbersome communication processes and challenging model convergence in practical edge deployments due to the training nature of its model information interactions. The paper proposes a hierarchical federated learning algorithm called FedDyn to address these challenges. FedDyn uses dynamic weighting to limit the negative effects of local model parameters with high dispersion and speed-up convergence. Additionally, an efficient aggregation-based hierarchical federated learning algorithm is proposed to improve training efficiency. The waiting time is set at the edge layer, enabling edge aggregation within a specified time, while the central server waits for the arrival of all edge aggregation models before integrating them. Dynamic grouping weighted aggregation is implemented during aggregation based on the average obsolescence of local models in various batches. The proposed algorithm is tested on the MNIST and CIFAR-10 datasets and compared with the FedAVG algorithm. The results show that FedDyn can reduce the negative effects of non-independent and identically distributed (IID) data on the model and shorten the total training time by 30% under the same accuracy rate compared to FedAVG. Full article
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18 pages, 4937 KiB  
Article
FL-SDUAN: A Fuzzy Logic-Based Routing Scheme for Software-Defined Underwater Acoustic Networks
Appl. Sci. 2023, 13(2), 944; https://doi.org/10.3390/app13020944 - 10 Jan 2023
Cited by 2 | Viewed by 998
Abstract
In underwater acoustic networks, the accurate estimation of routing weights is NP-hard due to the time-varying environment. Fuzzy logic is a powerful tool for dealing with vague problems. Software-defined networking (SDN) is a promising technology that enables flexible management by decoupling the data [...] Read more.
In underwater acoustic networks, the accurate estimation of routing weights is NP-hard due to the time-varying environment. Fuzzy logic is a powerful tool for dealing with vague problems. Software-defined networking (SDN) is a promising technology that enables flexible management by decoupling the data plane from the control plane. Inspired by this, we proposed a fuzzy logic-based software-defined routing scheme for underwater acoustic networks (FL-SDUAN). Specifically, we designed a software-defined underwater acoustic network architecture. Based on fuzzy path optimization (FPO-MST) and fuzzy cut-set optimization (FCO-MST), two minimum spanning tree algorithms under different network scales were proposed. In addition, we compared the proposed algorithms to state-of-the-art methods regarding packet delivery rate, end-to-end latency, and throughput in different underwater acoustic network scenarios. Extensive experiments demonstrated that a trade-off between performance and complexity was achieved in our work. Full article
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