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Special Issue "Applications of Smart Internet of Things"

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 14087

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Prof. Dr. Yeong-Seok Seo

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Guest Editor

Department of Computer Engineering, Yeungnam University, Gyeongsan, Gyeongbuk 38541, Republic of Korea
Interests: Internet of Things; big data; artificial intelligence; edge computing; software engineering
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jun-Ho Huh

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Guest Editor

Associate Professor (Tenure) of Dept. of Data Science, National Korea Maritime and Ocean University, Busan 49112, Korea
Interests: big data; virtualization; AI; shipbuilding industry; shipping industry; Industry 4.0
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the development of the Internet of Things (IoT), smart IoT is an exciting emerging field with great potential by allowing smart devices to better serve their users. Smart IoT aims to connect all objects in our daily life with the goal of “Everything smart” to make smart life and smart city, rather than simple connection between devices. This Special Issue focuses on these challenges.

Even though there has been a series of smart IoT studies in Computer Engineering, more sophisticated and practical studies are still needed to make our live more convenient and efficient, through convergence with the latest technologies, such as artificial Intelligence, big data analysis, edge computing, and smart security techniques.

Thus, this Special Issue aims to start a discussion about application and convergence studies that would contribute to smart IoT. For this purpose, this Special Issue is open to receiving a variety of valuable manuscripts concerning smart IoT issues based on the latest computing solutions.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

IoT solutions with artificial intelligence, big data, and blockchain;
IoT solutions with social networks, multimedia and mobile computing;
Effective data collection and application solutions using edge computing;
IoT solutions respecting human beings and their lives by aiding and serving neglected or isolated people;
Smart security and privacy systems to cover smart IoT environment;
Intelligent transportation techniques with internet of vehicles for smart city;
Software engineering techniques for improving the quality of smart IoT systems.

I look forward to receiving your contributions.

Prof. Dr. Yeong-Seok Seo
Prof. Dr. Jun-Ho Huh
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. Electronics 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 2200 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

smart IoT
artificial intelligence
big data
cloud computing
edge computing
security
software engineering
social network
multimedia
mobile computing

Published Papers (9 papers)

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Research

16 pages, 3363 KiB

Open AccessArticle

E-HRNet: Enhanced Semantic Segmentation Using Squeeze and Excitation

and

Electronics 2023, 12(17), 3619; https://doi.org/10.3390/electronics12173619 - 27 Aug 2023

Viewed by 597

Abstract

In the field of computer vision, convolutional neural network (CNN)-based models have demonstrated high accuracy and good generalization performance. However, in semantic segmentation, CNN-based models have a problem—the spatial and global context information is lost owing to a decrease in resolution during feature extraction. High-resolution networks (HRNets) can resolve this problem by keeping high-resolution processing layers parallel. However, information loss still occurs. Therefore, in this study, we propose an HRNet combined with an attention module to address the issue of information loss. The attention module is strategically placed immediately after each convolution to alleviate information loss by emphasizing the information retained at each stage. To achieve this, we employed a squeeze-and-excitation (SE) block as the attention module, which can seamlessly integrate into any model and enhance the performance without imposing significant parameter increases. It emphasizes the spatial and global context information by compressing and recalibrating features through global average pooling (GAP). A performance comparison between the existing HRNet model and the proposed model using various datasets show that the mean class-wise intersection over union (mIoU) and mean pixel accuracy (MeanACC) improved with the proposed model, however, there was a small increase in the number of parameters. With cityscapes dataset, MeanACC decreased by 0.1% with the proposed model compared to the baseline model, but mIoU increased by 0.5%. With the LIP dataset, the MeanACC and mIoU increased by 0.3% and 0.4%, respectively. The mIoU also decreased by 0.1% with the PASCAL Context dataset, whereas the MeanACC increased by 0.7%. Overall, the proposed model showed improved performance compared to the existing model. Full article

(This article belongs to the Special Issue Applications of Smart Internet of Things)

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15 pages, 893 KiB

Open AccessArticle

Bridge of Trust: Cross Domain Authentication for Industrial Internet of Things (IIoT) Blockchain over Transport Layer Security (TLS)

Fatemeh Stodt

and

Christoph Reich

Electronics 2023, 12(11), 2401; https://doi.org/10.3390/electronics12112401 - 25 May 2023

Viewed by 1045

Abstract

The Industrial Internet of Things (IIoT) holds significant potential for improving efficiency, quality, and flexibility. In decentralized systems, there are no trust-based centralized authentication techniques, which are unsuitable for distributed networks or subnets, as they have a single point of failure. However, in a decentralized system, more emphasis is needed on trust management, which presents significant challenges in ensuring security and trust in industrial devices and applications. To address these issues, industrial blockchain has the potential to make use of trustless and transparent technologies for devices, applications, and systems. By using a distributed ledger, blockchains can track devices and their data exchanges, improving relationships between trading partners, and proving the supply chain. In this paper, we propose a model for cross-domain authentication between the blockchain-based infrastructure and industrial centralized networks outside the blockchain to ensure secure communication in industrial environments. Our model enables cross authentication for different sub-networks with different protocols or authentication methods while maintaining the transparency provided by the blockchain. The core concept is to build a bridge of trust that enables secure communication between different domains in the IIoT ecosystem. Our proposed model enables devices and applications in different domains to establish secure and trusted communication channels through the use of blockchain technology, providing an efficient and secure way to exchange data within the IIoT ecosystem. Our study presents a decentralized cross-domain authentication mechanism for field devices, which includes enhancements to the standard authentication system. To validate the feasibility of our approach, we developed a prototype and assessed its performance in a real-world industrial scenario. By improving the security and efficiency in industrial settings, this mechanism has the potential to inspire this important area. Full article

(This article belongs to the Special Issue Applications of Smart Internet of Things)

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22 pages, 2101 KiB

Open AccessArticle

GUI Component Detection-Based Automated Software Crash Diagnosis

Seong-Guk Nam

and

Yeong-Seok Seo

Electronics 2023, 12(11), 2382; https://doi.org/10.3390/electronics12112382 - 24 May 2023

Viewed by 786

Abstract

This study presents an automated software crash-diagnosis technique using a state transition graph (STG) based on GUI-component detection. An STG is a graph representation of the state changes in an application that are caused by actions that are executed in the GUI, which avoids redundant test cases and generates bug-reproduction scenarios. The proposed technique configures the software application STG using computer vision and artificial intelligence technologies and performs automated GUI testing without human intervention. Four experiments were conducted to evaluate the performance of the proposed technique: a detection-performance analysis of the GUI-component detection model, code-coverage measurement, crash-detection-performance analysis, and crash-detection-performance analysis in a self-configured multi-crash environment. The GUI-component detection model obtained a macro F1-score of 0.843, even with a small training dataset for the deep-learning model in the detection-performance analysis. Furthermore, the proposed technique achieved better performance results than the baseline Monkey in terms of code coverage, crash detection, and multi-crash detection. Full article

(This article belongs to the Special Issue Applications of Smart Internet of Things)

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24 pages, 7037 KiB

Open AccessArticle

Software Testing Techniques for Improving the Quality of Smart-Home IoT Systems

Andrei-Mihai Vadan

and

Liviu-Cristian Miclea

Electronics 2023, 12(6), 1337; https://doi.org/10.3390/electronics12061337 - 11 Mar 2023

Cited by 1 | Viewed by 1394

Abstract

Software is present in any modern device and is one of the most important components of a new product. IoT systems for smart homes have become more popular in recent years, and testing these systems using advanced methods is very important because it should improve software quality from the beginning of development, resulting in a faster product development overall and a better user experience for the client. In this paper, we describe methods of how to build fast quality assurance software for automation testing in comparison with current trends. Those methods are applicable to teams that are using custom test automation frameworks and working in big projects. The methods have already been applied with success in testing infotainment systems in the automotive industry and our custom-made smart-home IoT system. We will present the system and testing techniques used for testing web interfaces, mobile applications, cross-platform mobile applications, and backend using a new design pattern, called ‘Locate, Execute, Expect’. We compare this new design pattern against Page Object Model and will guide you on how to integrate it in an existing project or how to use it with Gherkin. In conclusion, we will see the main advantages of using this technique and how much faster it is in a real-life scenario, we will learn how it can replace Gherkin, and we will also see the main disadvantages. Full article

(This article belongs to the Special Issue Applications of Smart Internet of Things)

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17 pages, 2277 KiB

Open AccessArticle

Kernel-Based Container File Access Control Architecture to Protect Important Application Information

Hoo-Ki Lee

Sung-Hwa Han

and

Daesung Lee

Electronics 2023, 12(1), 52; https://doi.org/10.3390/electronics12010052 - 23 Dec 2022

Viewed by 1385

Abstract

Container platforms ease the deployment of applications and respond to failures. The advantages of container platforms have promoted their use in information services. However, the use of container platforms is accompanied by associated security risks. For instance, malware uploaded by users can leak important information, and malicious operators can cause unauthorized modifications to important files to create service errors. These security threats degrade the quality of information services and reduce their reliability. To overcome these issues, important container files should be protected by file-access control functions. However, legacy file-access control techniques, such as umask and SecureOS, do not support container platforms. To address this problem, we propose a novel kernel-based architecture in this study to control access to container files. The proposed container file-access control architecture comprises three components. The functionality and performance of the proposed architecture were assessed by implementing it on a Linux platform. Our analysis confirmed that the proposed architecture adequately controls users’ access to container files and performs on par with legacy file-access control techniques. Full article

(This article belongs to the Special Issue Applications of Smart Internet of Things)

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19 pages, 843 KiB

Open AccessArticle

Kernel-Based Real-Time File Access Monitoring Structure for Detecting Malware Activity

Sung-Hwa Han

and

Daesung Lee

Electronics 2022, 11(12), 1871; https://doi.org/10.3390/electronics11121871 - 14 Jun 2022

Cited by 1 | Viewed by 1537

Abstract

Obfuscation and cryptography technologies are applied to malware to make the detection of malware through intrusion prevention systems (IPSs), intrusion detection systems (IDSs), and antiviruses difficult. To address this problem, the security requirements for post-detection and proper response are presented, with emphasis on the real-time file access monitoring function. However, current operating systems provide only file access control techniques, such as SELinux (version 2.6, Red Hat, Raleigh, NC, USA) and AppArmor (version 2.5, Immunix, Portland, OR, USA), to protect system files and do not provide real-time file access monitoring. Thus, the service manager or data owner cannot determine real-time unauthorized modification and leakage of important files by malware. In this paper, a structure to monitor user access to important files in real time is proposed. The proposed structure has five components, with a kernel module interrelated to the application process. With this structural feature, real-time monitoring is possible for all file accesses, and malicious attackers cannot bypass this file access monitoring function. By verifying the positive and negative functions of the proposed structure, it was validated that the structure accurately provides real-time file access monitoring function, the monitoring function resource is sufficiently low, and the file access monitoring performance is high, further confirming the effectiveness of the proposed structure. Full article

(This article belongs to the Special Issue Applications of Smart Internet of Things)

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25 pages, 9603 KiB

Open AccessArticle

Attack Graph Generation with Machine Learning for Network Security

and

Electronics 2022, 11(9), 1332; https://doi.org/10.3390/electronics11091332 - 22 Apr 2022

Cited by 2 | Viewed by 2287

Abstract

Recently, with the discovery of various security threats, diversification of hacking attacks, and changes in the network environment such as the Internet of Things, security threats on the network are increasing. Attack graph is being actively studied to cope with the recent increase in cyber threats. However, the conventional attack graph generation method is costly and time-consuming. In this paper, we propose a cheap and simple method for generating the attack graph. The proposed approach consists of learning and generating stages. First, it learns how to generate an attack path from the attack graph, which is created based on the vulnerability database, using machine learning and deep learning. Second, it generates the attack graph using network topology and system information with a machine learning model that is trained with the attack graph generated from the vulnerability database. We construct the dataset for attack graph generation with topological and system information. The attack graph generation problem is recast as a multi-output learning and binary classification problem. It shows attack path detection accuracy of 89.52% in the multi-output learning approach and 80.68% in the binary classification approach using the in-house dataset, respectively. Full article

(This article belongs to the Special Issue Applications of Smart Internet of Things)

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17 pages, 2950 KiB

Open AccessArticle

Prediction of Content Success and Cloud-Resource Management in Internet-of-Media-Things Environments

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Electronics 2022, 11(8), 1284; https://doi.org/10.3390/electronics11081284 - 18 Apr 2022

Cited by 3 | Viewed by 1533

Abstract

In Internet-of-Media-Things (IoMT) environments, users can access and view high-quality Over-the-Top (OTT) media services anytime and anywhere. As the number of OTT platform users has increased, the original content offered by such OTT platforms has become very popular, further increasing the number of users. Therefore, effective resource-management technology is an essential aspect for reducing service-operation costs by minimizing unused resources while securing the resources necessary to provide media services in a timely manner when the user’s resource-demand rates change rapidly. However, previous studies have investigated efficient cloud-resource allocation without considering the number of users after the release of popular content. This paper proposes a technology for predicting and allocating cloud resources in the form of a Long-Short-Term-Memory (LSTM)-based reinforcement-learning method that provides information for OTT service providers about whether users are willing to watch popular content using the Korean Bidirectional Encoder Representation from Transformer (KoBERT). Results of simulating the proposed technology verified that efficient resource allocation can be achieved by maintaining service quality while reducing cloud-resource waste depending on whether content popularity is disclosed. Full article

(This article belongs to the Special Issue Applications of Smart Internet of Things)

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25 pages, 1975 KiB

Open AccessArticle

Korean Prosody Phrase Boundary Prediction Model for Speech Synthesis Service in Smart Healthcare

Minho Kim

Youngim Jung

and

Hyuk-Chul Kwon

Electronics 2021, 10(19), 2371; https://doi.org/10.3390/electronics10192371 - 28 Sep 2021

Viewed by 1794

Abstract

Speech processing technology has great potential in the medical field to provide beneficial solutions for both patients and doctors. Speech interfaces, represented by speech synthesis and speech recognition, can be used to transcribe medical documents, control medical devices, correct speech and hearing impairments, and assist the visually impaired. However, it is essential to predict prosody phrase boundaries for accurate natural speech synthesis. This study proposes a method to build a reliable learning corpus to train prosody boundary prediction models based on deep learning. In addition, we offer a way to generate a rule-based model that can predict the prosody boundary from the constructed corpus and use the result to train a deep learning-based model. As a result, we have built a coherent corpus, even though many workers have participated in its development. The estimated pairwise agreement of corpus annotations is between 0.7477 and 0.7916 and kappa coefficient (K) between 0.7057 and 0.7569. In addition, the deep learning-based model based on the rules obtained from the corpus showed a prediction accuracy of 78.57% for the three-level prosody phrase boundary, 87.33% for the two-level prosody phrase boundary. Full article

(This article belongs to the Special Issue Applications of Smart Internet of Things)

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