Special Issue "AI/ML Techniques for Intelligent IoT Systems"

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

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 2640

Special Issue Editors

Dr. Kandaraj Piamrat
E-Mail Website
Guest Editor
Department of Computer Sciences, University of Nantes, 44322 Nantes, France
Interests: wireless networks; multimedia streaming; MAC design; QoE; vehicular networks; artificial intelligence; machine learning
Dr. Hyunhee Park
E-Mail Website
Guest Editor
Department of Information & Telecommunication Engineering, Myongji University, Yongin-si 17058, Gyeonggi-do, Korea
Interests: wireless communications; MAC protocol design; big data analysis; self-driving vehicles; security systems

Special Issue Information

Dear Colleagues,

With a massive amount of data being generated by an increasing number of applications, future systems can learn to be more intelligent and to guarantee better services for users. The techniques of artificial intelligence (AI) and machine learning (ML) can help in building such systems, as they deliver an excellent advantage for studying the essential characteristics of the system as well as data collected from it. More recently, intelligent IoT systems have emerged as a topic that considers the use of AI/ML to enhance performance, enable faster customization, and optimize user experience. This is an important step in the context of autonomic and cognitive system management.

This Special Issue targets research results in the above area of intelligent systems or networks. Contributions should focus on topics such as artificial intelligence techniques and models for IoT network and service management. This also includes personalization, smart service orchestration and delivery, dynamic service function chaining, intent- and policy-based management, and centralized vs. distributed control of SDN/NFV-based networks. Moreover, articles on analytics and big data approaches, knowledge creation, and decision-making are particularly welcome. This Special Issue aims to provide a comprehensive overview of the state-of-the-art development in IoT systems; it also aims to explore novel concepts and practices with a long-term goal of fully automated systems via the technological advances of AI/ML in a wide range of applications.

We invite authors from both industry and academia working on applying methods and techniques of AI/ML to computer systems to submit original research or review articles that cover design, implementation, and optimization with a specific focus on models, protocols, and optimization algorithms.

Potential topics include, but are not limited to, the following:

  • AI/ML for software-defined IoT;
  • AI/ML for network optimization in IoT;
  • Deep and reinforcement learning for IoT data;
  • Data mining and big data analytics in IoT networks;
  • AI/ML for network management and orchestration for IoT;
  • Machine learning for user behavior modeling and prediction in IoT;
  • Innovative architectures and infrastructures for intelligent IoT systems;
  • Self-learning and adaptive protocols and algorithms for intelligent IoT systems.

Dr. Kandaraj Piamrat
Dr. Hyunhee Park
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 2000 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.

Published Papers (2 papers)

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Research

Article
Restoration of Dimensions for Ancient Drawing Recognition
Electronics 2021, 10(18), 2269; https://doi.org/10.3390/electronics10182269 - 15 Sep 2021
Cited by 7 | Viewed by 764
Abstract
This study aims to investigate and determine the actual size of the “cheok” scale—The traditional weights and measures of Korea—To aid in data construction on the recognition of ancient drawings in the field of artificial intelligence. The cheok scale can be divided into [...] Read more.
This study aims to investigate and determine the actual size of the “cheok” scale—The traditional weights and measures of Korea—To aid in data construction on the recognition of ancient drawings in the field of artificial intelligence. The cheok scale can be divided into Yeongjocheok, Jucheok, Pobaekcheok, and Joryegicheok. This study calculated the actual dimensions used in the drawings of Tonga and Eonjo contained in Jaseungcha Dohae by Gyunam Ha BaeckWon, which helped us analyze the scale used in the southern region of Korea in the 1800s. The scales of 1/15 cheok and 1/10 cheok were used in the Tonga and Eonjo sections in Jaseungcha Dohae, and the actual dimensions in the drawing were converted to the scale used at the time. Owing to the conversion, the dimensions in the drawings of Tonga were converted to 30.658 cm per cheok, and ~31.84 cm per cheok for Eonjo. In this manner, the actual dimensions used in the southern region of Korea around the year 1800 were restored. Through this study, the reference values for drawing recognition of machinery drawings in Korea around 1800 were derived. Full article
(This article belongs to the Special Issue AI/ML Techniques for Intelligent IoT Systems)
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Article
Intelligent Mirai Malware Detection for IoT Nodes
Electronics 2021, 10(11), 1241; https://doi.org/10.3390/electronics10111241 - 24 May 2021
Cited by 7 | Viewed by 1019
Abstract
The advancement in recent IoT devices has led to catastrophic attacks on the devices resulting in breaches in user privacy and exhausting resources of various organizations, so that users and organizations expend increased time and money. One such harmful malware is Mirai, which [...] Read more.
The advancement in recent IoT devices has led to catastrophic attacks on the devices resulting in breaches in user privacy and exhausting resources of various organizations, so that users and organizations expend increased time and money. One such harmful malware is Mirai, which has created worldwide recognition by impacting the digital world. There are several ways to detect Mirai, but the Machine Learning approach has proved to be accurate and reliable in detecting malware. In this research, a novel-based approach of detecting Mirai using Machine Learning Algorithm is proposed and implemented in Matlab and Python. To evaluate the proposed approaches, Mirai and Benign datasets are considered and training is performed on the dataset comprised of a Training set, Cross-Validation set and Test set using Artificial Neural Network (ANN) consisting of neurons in the hidden layer, which provides consistent accuracy, precision, recall and F-1 score. In this research, an accurate number of hidden layers and neurons are chosen to avoid the problem of Overfitting. This research provides a comparative analysis between ANN and Random Forest models of the dataset formed by merging Mirai and benign datasets of the Mirai malware detection pertaining to seven IoT devices. The dataset used in this research is “N-BaIoT” dataset, which represents data in the features infected by Mirai Malware. The results are found to be accurate and reliable as the best performance was achieved with an accuracy of 92.8% and False Negative rate of 0.3% and F-1 score of 0.99. The expected outcomes of this project, include major findings towards cost-effective Learning solutions in detecting Mirai Malware strains. Full article
(This article belongs to the Special Issue AI/ML Techniques for Intelligent IoT Systems)
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