State-of-the-Art of Embedding AI Techniques for Designing and Building IoT Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 29769

Special Issue Editors


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Department of Accounting, Business Information Systems and Statistics, Alexandru Ioan Cuza University of Iasi, 700506 Iași, Romania
Interests: neural networks; machine learning; deep learning; sentiment analysis; IoT systems; information systems for management; enterprise resource planning
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Humber College Institute of Technology and Advanced Learning, Toronto, ON, Canada
Interests: information systems; artificial intelligence; IoT

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Department of Management, Faculty of Education, Economics and Technology, University of Granada, 51001 Ceuta, Spain
Interests: IoT; information systems; smart cities; innovations in business; radical technological innovations in business and society; quantitative research
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In today’s high-tech environment, the natural symbiosis between Artificial Intelligence (AI) and the Internet of Things (IoT) is enriching people’s lives. Reaching the full potential of combining these two important domains, AI and IoT, requires a significant amount of research efforts from both academia and practice in order to identify the existing gaps and to develop new architectures, solutions, and technologies.

The Internet of Things can be perceived as the nexus of physical objects (i.e., things) equipped with different electronic devices and specialized software applications that facilitates the communication between them and the human user. Embedding AI into the software of regular electronic IoT devices transforms them into “intelligent” ones. The fortunate association of the two leads to enhanced equipment for the benefits of humankind.

This Special Issue of Electronics, entitled “State-of-the-Art of Embedding AI Techniques for Designing and Building IoT Systems” intends, as a main objective, to reunite in a single volume the most recent advances in the form of original research manuscripts and also reviews on relevant topics (for this Special Issue). Therefore, topics of interest for this Special Issue can include but are not limited to:

  • Intelligent electronic solutions for the applications of the future;
  • IoT smart devices;
  • Smart cities, smart offices, smart homes, smart electronics;
  • Machine learning techniques for intelligent software development;
  • Advanced features of power systems;
  • Fuzzy systems;
  • Neural networks.

Dr. Vasile-Daniel Pavaloaia
Prof. Dr. Dragan Pamucar
Dr. Ionela Bacain
Prof. Dr. Rodrigo Martin-Rojas
Guest Editors

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Keywords

  • Intelligent electronic solutions for the applications of the future
  • IoT smart devices
  • Smart cities, smart offices, smart homes, smart electronics
  • Machine learning techniques for intelligent software development
  • Advanced features of power systems
  • Fuzzy systems
  • Neural networks

Published Papers (2 papers)

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Review

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37 pages, 4890 KiB  
Review
Artificial Intelligence as a Disruptive Technology—A Systematic Literature Review
by Vasile-Daniel Păvăloaia and Sabina-Cristiana Necula
Electronics 2023, 12(5), 1102; https://doi.org/10.3390/electronics12051102 - 23 Feb 2023
Cited by 23 | Viewed by 24008
Abstract
The greatest technological changes in our lives are predicted to be brought about by Artificial Intelligence (AI). Together with the Internet of Things (IoT), blockchain, and several others, AI is considered to be the most disruptive technology, and has impacted numerous sectors, such [...] Read more.
The greatest technological changes in our lives are predicted to be brought about by Artificial Intelligence (AI). Together with the Internet of Things (IoT), blockchain, and several others, AI is considered to be the most disruptive technology, and has impacted numerous sectors, such as healthcare (medicine), business, agriculture, education, and urban development. The present research aims to achieve the following: identify how disruptive technologies have evolved over time and their current acceptation (1); extract the most prominent disruptive technologies, besides AI, that are in use today (2); and elaborate on the domains that were impacted by AI and how this occurred (3). Based on a sentiment analysis of the titles and abstracts, the results reveal that the majority of recent publications have a positive connotation with regard to the disruptive impact of edge technologies, and that the most prominent examples (the top five) are AI, the IoT, blockchain, 5G, and 3D printing. The disruptive effects of AI technology are still changing how people interact in the corporate, consumer, and professional sectors, while 5G and other mobile technologies will become highly disruptive and will genuinely revolutionize the landscape in all sectors in the upcoming years. Full article
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48 pages, 3795 KiB  
Systematic Review
Open Data Based Machine Learning Applications in Smart Cities: A Systematic Literature Review
by Luminita Hurbean, Doina Danaiata, Florin Militaru, Andrei-Mihail Dodea and Ana-Maria Negovan
Electronics 2021, 10(23), 2997; https://doi.org/10.3390/electronics10232997 - 01 Dec 2021
Cited by 13 | Viewed by 4599
Abstract
Machine learning (ML) has already gained the attention of the researchers involved in smart city (SC) initiatives, along with other advanced technologies such as IoT, big data, cloud computing, or analytics. In this context, researchers also realized that data can help in making [...] Read more.
Machine learning (ML) has already gained the attention of the researchers involved in smart city (SC) initiatives, along with other advanced technologies such as IoT, big data, cloud computing, or analytics. In this context, researchers also realized that data can help in making the SC happen but also, the open data movement has encouraged more research works using machine learning. Based on this line of reasoning, the aim of this paper is to conduct a systematic literature review to investigate open data-based machine learning applications in the six different areas of smart cities. The results of this research reveal that: (a) machine learning applications using open data came out in all the SC areas and specific ML techniques are discovered for each area, with deep learning and supervised learning being the first choices. (b) Open data platforms represent the most frequently used source of data. (c) The challenges associated with open data utilization vary from quality of data, to frequency of data collection, to consistency of data, and data format. Overall, the data synopsis as well as the in-depth analysis may be a valuable support and inspiration for the future smart city projects. Full article
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