applsci-logo

Journal Browser

Journal Browser

Emerging Information Technologies for Next Generation Communications and Networks including Selected Papers from ICGHIT 2020.

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 (31 July 2020) | Viewed by 31885

Special Issue Editor


E-Mail Website
Guest Editor
Department of Software and Communications Energy, Hongik University, Sejongro 2639, Republic of Korea
Interests: wireless networks and communications; WSN; wireless ICN; edge computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 8th International Conference on Green and Human Information Technology (ICGHIT 2020) will be held 5–7 February, 2019, in Hanoi, Vietnam (http://icghit.org/ ). The 8th International Conference on Green and Human Information Technology is a unique global conference for researchers, industry professionals, and academics who are interested in the latest developments in green and human information technology. The theme of this year’s conference is “Creative and Hyper X Learning Technology.” The latest technologies of X-Learning using neural networks are already pervading our daily life regardless of our recognition. They present substantial challenges and great opportunities at the same time. Centering on this theme, we provide an exciting program: hands-on experience-based tutorial sessions and special sessions covering research issues and directions with applications from both theoretical and practical viewpoints. The conference will also include plenary sessions, technical sessions, and workshops with special sessions. The topics include, but are not limited to the following: Green information technology green technology, energy-saving green computing, green IT convergence and applications communication and IoT communications, networks optical, visual light communication ad-hoc, sensor Networks M2M/IoT, ubiquitous computing computer and network security wireless and mobile security internet of things security applied cryptography security in big data and cloud computing multimedia and signal processing multimedia processing, smart media technology, speech and signal processing, computer vision and image processing control and intelligent system automatic control, neural network and fuzzy, artificial intelligence, HCI intelligent robotics and transportation, HRI brain science and bioengineering SW/HW design, architecture, development architecture and protocols, sustainable sensor networks information-centric sensor networks, blockchain-based secure sensor networks, AI-based self-evolving sensor networks, sensor/RFID circuits, design system on a chip (SoC), IC system for communication.

Dr. Byung-Seo Kim
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

  • green information technology
  • communication and IoT
  • computer and network security
  • multimedia and signal processing
  • control and intelligent system
  • SW/HW design
  • architecture and development

Published Papers (7 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Editorial

Jump to: Research

4 pages, 168 KiB  
Editorial
Emerging Information Technologies for Next Generation Communications and Networks
by Byung-Seo Kim
Appl. Sci. 2021, 11(2), 812; https://doi.org/10.3390/app11020812 - 16 Jan 2021
Cited by 1 | Viewed by 1397
Abstract
Our lives can be said to be in an era with information and communication devices, and we are pursuing a hyperconnected society with advanced information and communication devices [...] Full article

Research

Jump to: Editorial

22 pages, 1536 KiB  
Article
EPF—An Efficient Forwarding Mechanism in SDN Controller Enabled Named Data IoTs
by Asadullah Tariq, Rana Asif Rehman and Byung-Seo Kim
Appl. Sci. 2020, 10(21), 7675; https://doi.org/10.3390/app10217675 - 30 Oct 2020
Cited by 9 | Viewed by 2506
Abstract
The vision of the Internet of Things (IoT) is that it connects all kinds of things by leveraging the creation of increasingly affordable and small devices that can be embedded for sensing, processing, wireless communication, and actuation. Named data networking (NDN) is a [...] Read more.
The vision of the Internet of Things (IoT) is that it connects all kinds of things by leveraging the creation of increasingly affordable and small devices that can be embedded for sensing, processing, wireless communication, and actuation. Named data networking (NDN) is a newly emerging Internet paradigm that may replace the current Internet architecture and that fulfills most of the expectations of the IoT. Software-defined networking (SDN) is an emerging paradigm of technology that is highly capable of managing overall networks efficiently and transforming complex network architectures into manageable, simple ones. The combination of the SDN controller, NDN, and IoT can be lethal in the overall performance of the network. Broadcast storms, due to the flooding nature of NDN’s wireless channel, are a serious issue when it comes to forwarding interest and data packets. Energy consumption of sensor nodes in dense IoT scenarios causes problems in forwarding as well as unnecessary delays, decreases network performance, and increases the cost and packet delay for important packets. We took these problems as our baseline and proposed an energy-efficient, priority-based forwarding (EPF) in SDN-enabled NDN–IoT. Our scheme EPF used the efficient flow management of the SDN controller to control the broadcast storm and efficiently forward the priority-based packets. A defer timer mechanism was used to prioritized the packet upon its arrival to the node. An energy threshold mechanism was used to control energy consumption and improve overall energy efficiency. We compared our scheme with the traditional flooding mechanism and geographic interest forwarding; EPF outclassed the other schemes and produced the best results in terms of total number of interests and retransmissions, content retrieval time, total number of priority interests, energy consumption, and network lifetime. Full article
Show Figures

Figure 1

12 pages, 8842 KiB  
Article
Deep Learning Model with Transfer Learning to Infer Personal Preferences in Images
by Jaeho Oh, Mincheol Kim and Sang-Woo Ban
Appl. Sci. 2020, 10(21), 7641; https://doi.org/10.3390/app10217641 - 29 Oct 2020
Cited by 5 | Viewed by 2302
Abstract
In this paper, we propose a deep convolutional neural network model with transfer learning that reflects personal preferences from inter-domain databases of images having atypical visual characteristics. The proposed model utilized three public image databases (Fashion-MNIST, Labeled Faces in the Wild [LFW], and [...] Read more.
In this paper, we propose a deep convolutional neural network model with transfer learning that reflects personal preferences from inter-domain databases of images having atypical visual characteristics. The proposed model utilized three public image databases (Fashion-MNIST, Labeled Faces in the Wild [LFW], and Indoor Scene Recognition) that include images with atypical visual characteristics in order to train and infer personal visual preferences. The effectiveness of transfer learning for incremental preference learning was verified by experiments using inter-domain visual datasets with different visual characteristics. Moreover, a gradient class activation mapping (Grad-CAM) approach was applied to the proposed model, providing explanations about personal visual preference possibilities. Experiments showed that the proposed preference-learning model using transfer learning outperformed a preference model not using transfer learning. In terms of the accuracy of preference recognition, the proposed model showed a maximum of about 7.6% improvement for the LFW database and a maximum of about 9.4% improvement for the Indoor Scene Recognition database, compared to the model that did not reflect transfer learning. Full article
Show Figures

Figure 1

13 pages, 618 KiB  
Article
Genuine Reversible Data Hiding Technique for H.264 Bitstream Using Multi-Dimensional Histogram Shifting Technology on QDCT Coefficients
by Jinwoo Kang, Hyunjung Kim and Sang-ug Kang
Appl. Sci. 2020, 10(18), 6410; https://doi.org/10.3390/app10186410 - 14 Sep 2020
Cited by 4 | Viewed by 1986
Abstract
Video has become the most important medium for communication among people. Video has become the most important medium for communication among people. Therefore, reversible data hiding technologies for video have been developed so that information can be hidden in the video without damaging [...] Read more.
Video has become the most important medium for communication among people. Video has become the most important medium for communication among people. Therefore, reversible data hiding technologies for video have been developed so that information can be hidden in the video without damaging the original video in order to be used in the copyright protection and distribution field of video. This paper proposes a practical and genuine reversible data hiding method by using a multi-dimensional histogram shifting scheme on QDCT coefficients in the H.264/AVC bitstream. The proposed method defines the vacant histogram bins as a set of n-dimensional vectors and finds the optimal vector space, which gives the best performance, in a 4 × 4 QDCT block. In addition, the secret message is mapped to the optimal vector space, which is equivalent to embedding the information into the QDCT block. The simulation results show that the data hiding efficiency is the highest among the compared five existing methods. In addition, the image distortion and maximum payload capacity are measured quite high. Full article
Show Figures

Figure 1

15 pages, 1340 KiB  
Article
Effective Privacy-Preserving Collection of Health Data from a User’s Wearable Device
by Jong Wook Kim, Su-Mee Moon, Sang-ug Kang and Beakcheol Jang
Appl. Sci. 2020, 10(18), 6396; https://doi.org/10.3390/app10186396 - 14 Sep 2020
Cited by 10 | Viewed by 2513
Abstract
The popularity of wearable devices equipped with a variety of sensors that can measure users’ health status and monitor their lifestyle has been increasing. In fact, healthcare service providers have been utilizing these devices as a primary means to collect considerable health data [...] Read more.
The popularity of wearable devices equipped with a variety of sensors that can measure users’ health status and monitor their lifestyle has been increasing. In fact, healthcare service providers have been utilizing these devices as a primary means to collect considerable health data from users. Although the health data collected via wearable devices are useful for providing healthcare services, the indiscriminate collection of an individual’s health data raises serious privacy concerns. This is because the health data measured and monitored by wearable devices contain sensitive information related to the wearer’s personal health and lifestyle. Therefore, we propose a method to aggregate health data obtained from users’ wearable devices in a privacy-preserving manner. The proposed method leverages local differential privacy, which is a de facto standard for privacy-preserving data processing and aggregation, to collect sensitive health data. In particular, to mitigate the error incurred by the perturbation mechanism of location differential privacy, the proposed scheme first samples a small number of salient data that best represents the original health data, after which the scheme collects the sampled salient data instead of the entire set of health data. Our experimental results show that the proposed sampling-based collection scheme achieves significant improvement in the estimated accuracy when compared with straightforward solutions. Furthermore, the experimental results verify that an effective tradeoff between the level of privacy protection and the accuracy of aggregate statistics can be achieved with the proposed approach. Full article
Show Figures

Figure 1

19 pages, 1951 KiB  
Article
Lightweight Detection Method of Obfuscated Landing Sites Based on the AST Structure and Tokens
by KyungHyun Han and Seong Oun Hwang
Appl. Sci. 2020, 10(17), 6116; https://doi.org/10.3390/app10176116 - 3 Sep 2020
Cited by 4 | Viewed by 2270
Abstract
Attackers use a variety of techniques to insert redirection JavaScript that leads a user to a malicious webpage, where a drive-by-download attack is executed. In particular, the redirection JavaScript in the landing site is obfuscated to avoid detection systems. In this paper, we [...] Read more.
Attackers use a variety of techniques to insert redirection JavaScript that leads a user to a malicious webpage, where a drive-by-download attack is executed. In particular, the redirection JavaScript in the landing site is obfuscated to avoid detection systems. In this paper, we propose a lightweight detection system based on static analysis to classify the obfuscation type and to promptly detect the obfuscated redirection JavaScript. The proposed model detects the obfuscated redirection JavaScript by converting the JavaScript into an abstract syntax tree (AST). Then, the structure and token information are extracted. Specifically, we propose a lightweight AST to identify the obfuscation type and the revised term frequency-inverse document frequency to efficiently detect the malicious redirection JavaScript. This approach enables rapid identification of the obfuscated redirection JavaScript and proactive blocking of the webpages that are used in drive-by-download attacks. Full article
Show Figures

Figure 1

14 pages, 2098 KiB  
Article
Bi-LSTM Model to Increase Accuracy in Text Classification: Combining Word2vec CNN and Attention Mechanism
by Beakcheol Jang, Myeonghwi Kim, Gaspard Harerimana, Sang-ug Kang and Jong Wook Kim
Appl. Sci. 2020, 10(17), 5841; https://doi.org/10.3390/app10175841 - 24 Aug 2020
Cited by 221 | Viewed by 18051
Abstract
There is a need to extract meaningful information from big data, classify it into different categories, and predict end-user behavior or emotions. Large amounts of data are generated from various sources such as social media and websites. Text classification is a representative research [...] Read more.
There is a need to extract meaningful information from big data, classify it into different categories, and predict end-user behavior or emotions. Large amounts of data are generated from various sources such as social media and websites. Text classification is a representative research topic in the field of natural-language processing that categorizes unstructured text data into meaningful categorical classes. The long short-term memory (LSTM) model and the convolutional neural network for sentence classification produce accurate results and have been recently used in various natural-language processing (NLP) tasks. Convolutional neural network (CNN) models use convolutional layers and maximum pooling or max-overtime pooling layers to extract higher-level features, while LSTM models can capture long-term dependencies between word sequences hence are better used for text classification. However, even with the hybrid approach that leverages the powers of these two deep-learning models, the number of features to remember for classification remains huge, hence hindering the training process. In this study, we propose an attention-based Bi-LSTM+CNN hybrid model that capitalize on the advantages of LSTM and CNN with an additional attention mechanism. We trained the model using the Internet Movie Database (IMDB) movie review data to evaluate the performance of the proposed model, and the test results showed that the proposed hybrid attention Bi-LSTM+CNN model produces more accurate classification results, as well as higher recall and F1 scores, than individual multi-layer perceptron (MLP), CNN or LSTM models as well as the hybrid models. Full article
Show Figures

Figure 1

Back to TopTop