Editorial

5 pages, 159 KiB

Open AccessEditorial

Advances in Architecture, Protocols, and Challenges in Internet of Things: From Technologies to Applications

by Weidong Fang and Chunsheng Zhu

Appl. Sci. 2024, 14(8), 3266; https://0-doi-org.brum.beds.ac.uk/10.3390/app14083266 - 12 Apr 2024

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With the rapid development and widespread application of Internet of Things (IoT) technology, we are in an era of digital transformation, where the integration between the physical and digital worlds continues to deepen [...] Full article

(This article belongs to the Special Issue Advances in Architecture, Protocols and Challenges in Internet of Things)

Research

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15 pages, 885 KiB

Open AccessArticle

HELPFuL: Human Emotion Label Prediction Based on Fuzzy Learning for Realizing Artificial Intelligent in IoT

by Lingjun Zhang, Hua Zhang, Yifan Wu, Yanping Xu, Tingcong Ye, Mengjing Ma and Linhao Li

Appl. Sci. 2023, 13(13), 7799; https://0-doi-org.brum.beds.ac.uk/10.3390/app13137799 - 01 Jul 2023

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Abstract

Human emotion label prediction is crucial to Artificial Intelligent in the Internet of Things (IoT). Facial expression recognition is the main technique to predict human emotion labels. Existing facial expression recognition methods do not consider the compound emotion and the fuzziness of emotion [...] Read more.

Human emotion label prediction is crucial to Artificial Intelligent in the Internet of Things (IoT). Facial expression recognition is the main technique to predict human emotion labels. Existing facial expression recognition methods do not consider the compound emotion and the fuzziness of emotion labels. Fuzzy learning is a mathematical tool for dealing with fuzziness and uncertainty information. The advantage of using fuzzy learning for human emotion recognition is that multiple fuzzy sentiment labels can be processed simultaneously. This paper proposes a fuzzy learning-based expression recognition method for human emotion label prediction. First, a fuzzy label distribution system is constructed using fuzzy sets for representing facial expressions. Then, two fuzzy label distribution prediction methods based on fuzzy rough sets are proposed to solve the compound emotion prediction. The probability that a sample is likely and definitely belongs to an emotion is obtained by calculating the upper and lower approximations. Experiments show the proposed algorithm not only performs well on human emotion label prediction but can also be used for other label distribution prediction tasks. The proposed method is more accurate and more general than other methods. The improvement of the method on the effect of emotion recognition extends the application scope of artificial intelligence in IoT. Full article

(This article belongs to the Special Issue Advances in Architecture, Protocols and Challenges in Internet of Things)

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19 pages, 20861 KiB

Open AccessArticle

A Novel Adaptive Group Sparse Representation Model Based on Infrared Image Denoising for Remote Sensing Application

by Juan Chen, Zhencai Zhu, Haiying Hu, Lin Qiu, Zhenzhen Zheng and Lei Dong

Appl. Sci. 2023, 13(9), 5749; https://0-doi-org.brum.beds.ac.uk/10.3390/app13095749 - 06 May 2023

Cited by 1 | Viewed by 1265

Abstract

Infrared (IR) Image preprocessing is aimed at image denoising and enhancement to help with small target detection. According to the sparse representation theory, the IR original image is low rank, and the coefficient shows a sparse character. The low rank and sparse model [...] Read more.

Infrared (IR) Image preprocessing is aimed at image denoising and enhancement to help with small target detection. According to the sparse representation theory, the IR original image is low rank, and the coefficient shows a sparse character. The low rank and sparse model could distinguish between the original image and noise. The IR images lack texture and details. In IR images, the small target is hard to recognize. Traditional denoising methods based on nuclear norm minimization (NNM) treat all eigenvalues equally, which blurs the concrete details. They are unable to achieve a good denoising performance. Deep learning methods necessitate a large number of train images, which are difficult to obtain in IR image denoising. It is difficult to perform well under high noise in IR image denoising. Tracking and detection would not be possible without a proper denoising method. This article fuses the weighted nuclear norm minimization (WNNM) with an adaptive similar patch, searching based on the group sparse representation for infrared images. We adaptively selected similar structural blocks based on certain computational criteria, and we used the K-nearest neighbor (KNN) cluster to constitute more similar groups, which is helpful in recovering the complex background with high Gaussian noise. Then, we shrank all eigenvalues with different weights in the WNNM model to solve the optimization problem. Our method could recover more detailed information in the images. The algorithm not only obtains good denoising results in common image denoising but also achieves good performance in infrared image denoising. The target in IR images attains a high signal for the clutter in IR detection systems for remote sensing. Under common data sets and real infrared images, it has a good noise suppression effect with a high peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM), with higher noise and a much more complex background. Full article

(This article belongs to the Special Issue Advances in Architecture, Protocols and Challenges in Internet of Things)

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12 pages, 2233 KiB

Open AccessArticle

An Emotion Speech Synthesis Method Based on VITS

by Wei Zhao and Zheng Yang

Appl. Sci. 2023, 13(4), 2225; https://0-doi-org.brum.beds.ac.uk/10.3390/app13042225 - 09 Feb 2023

Cited by 5 | Viewed by 5701

Abstract

People and things can be connected through the Internet of Things (IoT), and speech synthesis is one of the key technologies. At this stage, end-to-end speech synthesis systems are capable of synthesizing relatively realistic human voices, but the current commonly used parallel text-to-speech [...] Read more.

People and things can be connected through the Internet of Things (IoT), and speech synthesis is one of the key technologies. At this stage, end-to-end speech synthesis systems are capable of synthesizing relatively realistic human voices, but the current commonly used parallel text-to-speech suffers from loss of useful information during the two-stage delivery process, and the control features of the synthesized speech are monotonous, with insufficient expression of features, including emotion, leading to emotional speech synthesis becoming a challenging task. In this paper, we propose a new system named Emo-VITS, which is based on the highly expressive speech synthesis module VITS, to realize the emotion control of text-to-speech synthesis. We designed the emotion network to extract the global and local features of the reference audio, and then fused the global and local features through the emotion feature fusion module based on the attention mechanism, so as to achieve more accurate and comprehensive emotion speech synthesis. The experimental results show that the Emo-VITS system’s error rate went up a little bit compared with the network without emotionality and does not affect the semantic understanding. However, this system is superior to other networks in naturalness, sound quality, and emotional similarity. Full article

(This article belongs to the Special Issue Advances in Architecture, Protocols and Challenges in Internet of Things)

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9 pages, 2346 KiB

Open AccessCommunication

Road Pavement Damage Detection Based on Local Minimum of Grayscale and Feature Fusion

by Wei-Wei Jin, Guo-Hong Chen, Zhuo Chen, Yun-Lei Sun, Jie Ni, Hao Huang, Wai-Hung Ip and Kai-Leung Yung

Appl. Sci. 2022, 12(24), 13006; https://0-doi-org.brum.beds.ac.uk/10.3390/app122413006 - 18 Dec 2022

Cited by 3 | Viewed by 1162

Abstract

In this work, we propose a road pavement damage detection deep learning model based on feature points from a local minimum of grayscale. First, image blocks, consisting of the neighborhood of feature points, are cut from the image window to form an image [...] Read more.

In this work, we propose a road pavement damage detection deep learning model based on feature points from a local minimum of grayscale. First, image blocks, consisting of the neighborhood of feature points, are cut from the image window to form an image block dataset. The image blocks are then input into a convolutional neural network (CNN) to train the model, extracting the image block features. In the testing process, the feature points as well as the image blocks are selected from a test image, and the trained CNN model can output the feature vectors for these feature image blocks. All the feature vectors will be combined to a composite feature vector as the feature descriptor of the test image. At last, the classifier of the model, constructed by a support vector machine (SVM), gives the classification as to whether the image window contains damaged areas or not. The experimental results suggest that the proposed pavement damage detection method based on feature-point image blocks and feature fusion is of high accuracy and efficiency. We believe that it has application potential in general road damage detection, and further investigation is desired in the future. Full article

(This article belongs to the Special Issue Advances in Architecture, Protocols and Challenges in Internet of Things)

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24 pages, 9486 KiB

Open AccessArticle

Network Slicing Resource Allocation Based on LSTM-D3QN with Dual Connectivity in Heterogeneous Cellular Networks

by Geng Chen, Xinzheng Mu, Fei Shen and Qingtian Zeng

Appl. Sci. 2022, 12(18), 9315; https://0-doi-org.brum.beds.ac.uk/10.3390/app12189315 - 16 Sep 2022

Cited by 2 | Viewed by 2001

Abstract

With the explosive growth of network traffic and the diversification of service demands, network slicing (NS) and dual connectivity (DC) are considered as promising technologies in wireless networks. In this paper, we propose a novel algorithm that solves the resource allocation problem of [...] Read more.

With the explosive growth of network traffic and the diversification of service demands, network slicing (NS) and dual connectivity (DC) are considered as promising technologies in wireless networks. In this paper, we propose a novel algorithm that solves the resource allocation problem of NS in heterogeneous networks with the assistance of DC, while satisfying the characteristic requirements of eMBB and URLLC services. Firstly, we model the scenario and formulate the optimization problem, which is proved as an NP-Hard problem. Secondly, due to the nonconvex and combinatorial nature, the dueling double deep Q-network with long short-term memory (LSTM-D3QN) is proposed to solve this problem, aiming to improve the overall network utility, while ensuring the quality of experience (QoE). Then, we analyze the complexity of the algorithm. Finally, the simulation results show that the proposed algorithm can maximize the total utility of the system, while guaranteeing the user QoE. Compared with LSTM-A2C and DQN, the proposed algorithm improves the long-term network utility by 2.6% and 7.2%, respectively. In addition, compared with the algorithm without DC under the conditions of no priority, eMBB priority and URLLC priority, the proposed algorithm improves the network utility by 4.2%, 2.1% and 4.1%, respectively. Full article

(This article belongs to the Special Issue Advances in Architecture, Protocols and Challenges in Internet of Things)

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12 pages, 3578 KiB

Open AccessArticle

Periodic Monitoring and Filtering Suppression of Signal Interference in Mine 5G Communication

by Liya Zhang, Wei Yang, Weidong Fang, Yufeng Jiang and Qing Zhao

Appl. Sci. 2022, 12(15), 7689; https://0-doi-org.brum.beds.ac.uk/10.3390/app12157689 - 30 Jul 2022

Cited by 2 | Viewed by 1263

Abstract

Diverse IoT applications, such as unmanned driving, intelligent video, unmanned working face, industrial control, and intelligent robot inspection, are the key technologies in the field of intelligent mines. In order to fully meet the requirements of underground IoT systems of high bandwidth, low [...] Read more.

Diverse IoT applications, such as unmanned driving, intelligent video, unmanned working face, industrial control, and intelligent robot inspection, are the key technologies in the field of intelligent mines. In order to fully meet the requirements of underground IoT systems of high bandwidth, low latency, and massive connections, it is necessary to study 5G technologies suitable for underground environments to achieve effective deployment in mines. In key areas, such as main transport roadways, fully mechanized mining faces, and underground substations, both spurs and crosstalk in the frequency domain are the dominant factors affecting the stability and reliability of 5G signals. For the purpose of improving the performance of mine 5G, a fusion anti-interference scheme is designed here. Based on a deep complex network and blind source separation, periodic monitoring and filtering suppression of signal interference can be achieved. The test results show that the frequency domain spurs’ suppression capability of the proposed method is 20% higher than that of the traditional equalization method. For frequency domain crosstalk, 90% interference elimination could be achieved by the proposed method without additional delays when compared with the conventional blind source separation. The high-bandwidth and low-latency characteristics of 5G communication can be guaranteed by this method. Full article

(This article belongs to the Special Issue Advances in Architecture, Protocols and Challenges in Internet of Things)

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Review

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23 pages, 590 KiB

Open AccessFeature PaperReview

Key Agreement and Authentication Protocols in the Internet of Things: A Survey

by Sabina Szymoniak and Shalini Kesar

Appl. Sci. 2023, 13(1), 404; https://0-doi-org.brum.beds.ac.uk/10.3390/app13010404 - 28 Dec 2022

Cited by 10 | Viewed by 2863

Abstract

The rapid development of Internet of things (IoT) technology has made the IoT applicable in many areas of life and has contributed to the IoT’s improvement. IoT devices are equipped with various sensors that enable them to perform the tasks they were designed [...] Read more.

The rapid development of Internet of things (IoT) technology has made the IoT applicable in many areas of life and has contributed to the IoT’s improvement. IoT devices are equipped with various sensors that enable them to perform the tasks they were designed for. The use of such devices is associated with securing communication between devices and users. The key stages of communication are the processes of authentication and the process of agreeing on session keys because they are the basis of the subsequent communication phases. The specially designed security protocols are used to secure communication. These protocols define the course of communication and cryptographic techniques employed for securing. In this article, we have reviewed the latest communication protocols designed to secure authentication processes and agree on session keys in IoT environments. We analyzed the proposed protocols’ security level, vulnerability, and computational and communication costs. We showed our observations, describing the requirements that a secure protocol should meet. Full article

(This article belongs to the Special Issue Advances in Architecture, Protocols and Challenges in Internet of Things)

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