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Applied Sciences
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Advances in Intelligent Internet of Things Ⅱ
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Advances in Intelligent Internet of Things Ⅱ

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: closed (20 May 2022) | Viewed by 9708

Special Issue Editors

Dr. Bo Wei

E-Mail Website
Guest Editor
Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, UK
Interests: Mobile computing; internet of things; wireless sensor networks; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wai Lok Woo

E-Mail Website
Co-Guest Editor
Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, UK
Interests: machine learning; artificial intelligence; computational intelligence; data analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The ubiquitous Internet of Things (IoT) has enabled many applications, such as smart homes/cities, environmental monitoring, and industrial automation. Compared with the use of traditional IoT for data collection, the core of intelligent IoT is its application of signal modalities such as video, images, audio, wireless radio, and motion measurements for accurate and efficient perception. The use of intelligent IoT bridges the boundary between machine learning/artificial intelligence (AI) algorithms and resource-constrained embedded IoT devices. Computational power availability and energy consumption are two important factors in intelligent IoT. In order to facilitate efficient perception, intelligent IoT introduces the optimization of machine learning and AI algorithms. Recently, tailored machine learning/AI algorithms have been extensively explored to enable accurate, efficient, and real-time recognition in intelligent IoT devices. The derived algorithms are expected to achieve optimal performance between accuracy and efficiency. Energy-saving communication has also been investigated in IoT device connectivity to facilitate data analytics on cloud/edge servers because conventional wireless communication among IoT devices consumes a great deal of energy.

The focus of this Special Issue will be on a broad range of topics including the internet of things, machine learning, and data fusion, involving the introduction and development of new advanced theoretical algorithms and experimental application. Potential topics include, but are not limited to:

  • Internet of Things: machine learning and artificial-intelligence-driven applications;
  • Chatbots technology;
  • Natural language processing;
  • Autonomous vehicle technology;
  • Wearable sensors and IoT for monitoring and computing;
  • Pervasive mobile computing and wireless sensor networks: communication and applications;
  • Human–computer interaction for context awareness;
  • Edge computing for efficient perception;
  • Cyber-physical-social systems and constructs;
  • Other emerging applications of Intelligent Internet of Things.

Dr. Bo Wei
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
  • Machine learning
  • Computational intelligence
  • Mobile computing
  • Social signal processing
  • Wireless sensor networks
  • Human–computer interaction
  • Signal, image, and video processing

Published Papers (3 papers)

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20 pages, 4313 KiB  
Article
Data Naming Mechanism of LEO Satellite Mega-Constellations for the Internet of Things
by Mingfei Xia, Shengbo Hu, Hongqiu Luo, Tingting Yan and Yanfeng Shi
Appl. Sci. 2022, 12(14), 7083; https://doi.org/10.3390/app12147083 - 13 Jul 2022
Cited by 3 | Viewed by 1900
Abstract
The low earth orbit (LEO) mega constellation for the internet of thing (IoT) has become one of the hot spots for B5G and 6G concerns. Information-centric networking (ICN) provides a new approach to the interconnection of everything in the LEO mega constellation. In [...] Read more.
The low earth orbit (LEO) mega constellation for the internet of thing (IoT) has become one of the hot spots for B5G and 6G concerns. Information-centric networking (ICN) provides a new approach to the interconnection of everything in the LEO mega constellation. In ICN, data objects are independent of location, application, storage and transport methods. Therefore, data naming is one of the fundamental issues of ICN, and research on the data naming mechanism of the LEO mega constellation for the IoT is thus the focus of this study. Adopting a fusion of hierarchical, multicomponent, and hash flat as one structure, a data naming mechanism is proposed, which can meet the needs of the IoT multiservice attributes and high-performance transmission. Additionally, prefix tokens are used to describe hierarchical names with various embedded semantic functions to support multisource content retrieval for in-network functions. To verify the performance of the proposed data naming mechanism, an NS-3-based simulation platform for LEO mega constellations for the IoT is designed and developed. The test simulation results show that, compared with the IP address, the ICN-HMcH naming mechanism can increase throughput by as much as 54% and reduce the transmission delay of the LEO mega satellites for the IoT by 53.97%. The proposed data naming mechanism can provide high quality of service (QoS) transmission performance for the LEO mega constellation for IoT and performs better than IP-based transmission. Full article
(This article belongs to the Special Issue Advances in Intelligent Internet of Things Ⅱ)
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20 pages, 4287 KiB  
Article
A Comparison of Feature Selection and Forecasting Machine Learning Algorithms for Predicting Glycaemia in Type 1 Diabetes Mellitus
by Ignacio Rodríguez-Rodríguez, José-Víctor Rodríguez, Wai Lok Woo, Bo Wei and Domingo-Javier Pardo-Quiles
Appl. Sci. 2021, 11(4), 1742; https://doi.org/10.3390/app11041742 - 16 Feb 2021
Cited by 13 | Viewed by 3480
Abstract
Type 1 diabetes mellitus (DM1) is a metabolic disease derived from falls in pancreatic insulin production resulting in chronic hyperglycemia. DM1 subjects usually have to undertake a number of assessments of blood glucose levels every day, employing capillary glucometers for the monitoring of [...] Read more.
Type 1 diabetes mellitus (DM1) is a metabolic disease derived from falls in pancreatic insulin production resulting in chronic hyperglycemia. DM1 subjects usually have to undertake a number of assessments of blood glucose levels every day, employing capillary glucometers for the monitoring of blood glucose dynamics. In recent years, advances in technology have allowed for the creation of revolutionary biosensors and continuous glucose monitoring (CGM) techniques. This has enabled the monitoring of a subject’s blood glucose level in real time. On the other hand, few attempts have been made to apply machine learning techniques to predicting glycaemia levels, but dealing with a database containing such a high level of variables is problematic. In this sense, to the best of the authors’ knowledge, the issues of proper feature selection (FS)—the stage before applying predictive algorithms—have not been subject to in-depth discussion and comparison in past research when it comes to forecasting glycaemia. Therefore, in order to assess how a proper FS stage could improve the accuracy of the glycaemia forecasted, this work has developed six FS techniques alongside four predictive algorithms, applying them to a full dataset of biomedical features related to glycaemia. These were harvested through a wide-ranging passive monitoring process involving 25 patients with DM1 in practical real-life scenarios. From the obtained results, we affirm that Random Forest (RF) as both predictive algorithm and FS strategy offers the best average performance (Root Median Square Error, RMSE = 18.54 mg/dL) throughout the 12 considered predictive horizons (up to 60 min in steps of 5 min), showing Support Vector Machines (SVM) to have the best accuracy as a forecasting algorithm when considering, in turn, the average of the six FS techniques applied (RMSE = 20.58 mg/dL). Full article
(This article belongs to the Special Issue Advances in Intelligent Internet of Things Ⅱ)
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19 pages, 3693 KiB  
Article
A Novel Automatic Modulation Classification Method Using Attention Mechanism and Hybrid Parallel Neural Network
by Rui Zhang, Zhendong Yin, Zhilu Wu and Siyang Zhou
Appl. Sci. 2021, 11(3), 1327; https://doi.org/10.3390/app11031327 - 2 Feb 2021
Cited by 23 | Viewed by 3507
Abstract
Automatic Modulation Classification (AMC) is of paramount importance in wireless communication systems. Existing methods usually adopt a single category of neural network or stack different categories of networks in series, and rarely extract different types of features simultaneously in a proper way. When [...] Read more.
Automatic Modulation Classification (AMC) is of paramount importance in wireless communication systems. Existing methods usually adopt a single category of neural network or stack different categories of networks in series, and rarely extract different types of features simultaneously in a proper way. When it comes to the output layer, softmax function is applied for classification to expand the inter-class distance. In this paper, we propose a hybrid parallel network for the AMC problem. Our proposed method designs a hybrid parallel structure which utilizes Convolution Neural Network (CNN) and Gate Rate Unit (GRU) to extract spatial features and temporal features respectively. Instead of superposing these two categories of features directly, three different attention mechanisms are applied to assign weights for different types of features. Finally, a cosine similarity metric named Additive Margin softmax function, which can expand the inter-class distance and compress the intra-class distance simultaneously, is adopted for output. Simulation results demonstrate that the proposed method can achieve remarkable performance on an open access dataset. Full article
(This article belongs to the Special Issue Advances in Intelligent Internet of Things Ⅱ)
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