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Advances in Architecture, Protocols and Challenges in Internet of Things
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Advances in Architecture, Protocols and Challenges in Internet of Things

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

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 23233

Special Issue Editors

Dr. Weidong Fang

E-Mail Website
Guest Editor
Science and Technology on Micro-System Laboratory, Shanghai Institute of Micro-System and Information Technology, Chinese Academy of Sciences, Shanghai 201800, China
Interests: wireless sensor network; cyber security; trust; secure routing protocol
Special Issues, Collections and Topics in MDPI journals
Dr. Chunsheng Zhu

E-Mail Website
Guest Editor
SUSTech Institute of Future Networks, Southern University of Science and Technology, Shenzhen 518055, China
Interests: Internet of Things; wireless sensor networks; cloud computing; big data; social networks and security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Driven by digitalization, the power of Internet of Things (IoT) is being unleashed. Statistica market research predicts that the global IoT market will be worth around USD 1.6 trillion by 2025. Many consumers are enjoying these increasingly comfortable, efficient and convenient smart devices.

Considering the vast amount of smart devices (such as sensors, actuators, smart phone, terminal device with computing, etc.) connected to IoT, determining how to collect and aggregate massive sensed data and transmit them to users’ data centers is increasingly critical. However, it is more important to analyze and process these raw data to abstract more valuable information in order to make reasonable decisions. Unfortunately, many issues and challenges remain unsolved or are yet to be fully addressed due to the unique features and complex application scenarios of IoT systems. Firstly, IoT requires more appropriate architecture to meet the ultra-high speed, ultra-low latency, and ultra-large connection characteristics of 5G, such as a knowledge and service oriented, a content-driven architecture. Secondly, designing efficient protocols to cater to the demand of edge computing and artificial intelligence is extremely challenging.

The goal of this Special Issue of Applied Sciences is to feature the latest advances and directions in IoT architecture, protocols and challenges for typical applications (i.e., smart cities, precision agriculture, smart grid, and intelligent transportation), their performance, demands and implications on future network design.

Papers should contain original results or review/tutorial content to be accessible to general audiences working in the field. Topics of interest include (but are not limited to):

  • Scalable IoT architecture;
  • Intelligent big IoT data solutions based on edge computing;
  • Edge network virtualization;
  • Protocols for IoT: energy efficiency, interoperability, autonomous management;
  • Sensed data collection, management and analytics;
  • Security, privacy and trust in IoT;
  • Cloud computing and services for IoT;
  • New IoT applications and use cases.

Dr. Weidong Fang
Dr. Chunsheng Zhu
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
  • architecture
  • protocols
  • security
  • edge computing

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Published Papers (8 papers)

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Editorial

Jump to: Research, Review

5 pages, 159 KiB  
Editorial
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://doi.org/10.3390/app14083266 - 12 Apr 2024
Viewed by 1186
Abstract
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

Jump to: Editorial, Review

15 pages, 885 KiB  
Article
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://doi.org/10.3390/app13137799 - 1 Jul 2023
Viewed by 1187
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  
Article
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://doi.org/10.3390/app13095749 - 6 May 2023
Cited by 3 | Viewed by 1718
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  
Article
An Emotion Speech Synthesis Method Based on VITS
by Wei Zhao and Zheng Yang
Appl. Sci. 2023, 13(4), 2225; https://doi.org/10.3390/app13042225 - 9 Feb 2023
Cited by 5 | Viewed by 7436
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  
Communication
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://doi.org/10.3390/app122413006 - 18 Dec 2022
Cited by 5 | Viewed by 1510
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  
Article
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://doi.org/10.3390/app12189315 - 16 Sep 2022
Cited by 4 | Viewed by 2775
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  
Article
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://doi.org/10.3390/app12157689 - 30 Jul 2022
Cited by 3 | Viewed by 1786
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  
Review
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://doi.org/10.3390/app13010404 - 28 Dec 2022
Cited by 19 | Viewed by 3929
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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