Recent Advances in the IoT and Smart City Based on Artificial Intelligence

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

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 12433

Special Issue Editors

Department of Informatics, University of Rijeka Radmile Matejčić 2, 51000 Rijeka, Croatia
Interests: artificial intelligence; data science; machine learning; explainable artificial intelligence; explainable machine learning; human-centric AI; trustworthy Internet of Things systems
Faculty of Informatics and Digital Technologies, University of Rijeka, Radmile Matejčić 2, 51000 Rijeka, Croatia
Interests: artificial intelligence; machine learning; interpretable machine learning; educational data mining; natural language processing; machine translation
Special Issues, Collections and Topics in MDPI journals
Department of Informatics, University of Rijeka Radmile Matejčić 2, 51000 Rijeka, Croatia
Interests: artificial intelligence; data science; natural language processing; machine learning; machine translation

Special Issue Information

Dear Colleagues,

The technology of IoT and smart city systems is based on sensors through which an enormous amount of data is collected. Given the inhomogeneity of data coming from different sources and different devices, it is difficult to determine the right methods for their collection, processing, and analysis. The IoT and smart city can be supported by Artificial Intelligence (AI) methods to create devices that simulate intelligent behavior and enable decision making with little or no human intervention. The main role in guiding decision making and autonomous action is played by data analysis, which refers to the extraction of patterns and inferences from collected data through the use of statistical methods and deep learning. Studying and analyzing current information can be used to make better decisions in the future and drive intelligent actions depending on the decisions made. When talking about data analysis, it is important to mention text mining, because many of the data analyzed are in text form.

In smart city, IoT devices are interconnected and communicate for various tasks. The use of IoT applications is increasing exponentially, generating a large amount of connected data and, hence, the risk of data breaches and information leakage. Artificial Intelligence is used to develop complex algorithms to protect networks and IoT and smart city systems.

This Special Issue of Electronics covers research and discussion on applying AI methods to data collected by IoT and smart city devices in various domains, as well as improving the security of IoT and smart city systems. Both original research articles and comprehensive review papers are welcome.

Topics of interest of this Special Issue include but are not limited to:

  • Data analysis methods for IoT and smart city systems;
  • Text mining in IoT and smart cities;
  • AI decision support systems;
  • Security of IoT and smart city systems supported by Artificial Intelligence;
  • Real-world applications of Artificial Intelligence in areas such as:
    • IoT;
    • Smart cities;
    • Smart homes;
    • Smart agriculture;
    • Smart transportation;
    • Smart industry;
    • Smart health;
    • Smart city services;
    • Smart energy.

Prof. Dr. Maja Matetić
Prof. Dr. Marija Brkić Bakarić
Prof. Dr. Lucia Nacinovic Prskalo
Guest Editors

Manuscript Submission Information

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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 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 (IoT)
  • Smart city
  • Artificial Intelligence in the IoT and smart city
  • Data collection
  • Data processing
  • Data analysis
  • Text mining
  • Decision support systems
  • Intelligent action IoT and smart city security
  • Application of AI methods in the IoT and smart city

Published Papers (6 papers)

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Research

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14 pages, 1129 KiB  
Article
System for Automatic Assignment of Lexical Stress in Croatian
Electronics 2022, 11(22), 3687; https://doi.org/10.3390/electronics11223687 - 10 Nov 2022
Viewed by 1074
Abstract
It is very popular today to integrate voice interfaces into IoT devices. The pronunciation and proper prosody of speech play a major role in the intelligibility and naturalness of synthesized voices. Each language has its own prosodic characteristics. In this paper, we present [...] Read more.
It is very popular today to integrate voice interfaces into IoT devices. The pronunciation and proper prosody of speech play a major role in the intelligibility and naturalness of synthesized voices. Each language has its own prosodic characteristics. In this paper, we present the results of a study aimed at testing the applicability of methods for modelling and predicting the prosodic features of the Croatian language. The extent to which their performance can be improved by incorporating linguistic features and linguistic peculiarities specific to the Croatian language was investigated. In the model learning process, tree classification was used to predict the lexical stress position and the type of stress in a word, and a lexicon of 1,011,785 word forms was used as the model learning set. Separate models were created for predicting the position and type of lexical stress. The results improved significantly after the rules for atonic words (clitics) were applied. A hybrid approach combining a rule-based approach and a modelling approach was also proposed. The final accuracy of assigning lexical stress using the hybrid approach was 95.3%. Full article
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16 pages, 1456 KiB  
Article
T3OMVP: A Transformer-Based Time and Team Reinforcement Learning Scheme for Observation-Constrained Multi-Vehicle Pursuit in Urban Area
Electronics 2022, 11(9), 1339; https://doi.org/10.3390/electronics11091339 - 22 Apr 2022
Cited by 6 | Viewed by 1586
Abstract
Smart Internet of Vehicles (IoVs) combined with Artificial Intelligence (AI) will contribute to vehicle decision-making in the Intelligent Transportation System (ITS). Multi-vehicle pursuit (MVP) games, a multi-vehicle cooperative ability to capture mobile targets, are gradually becoming a hot research topic. Although there are [...] Read more.
Smart Internet of Vehicles (IoVs) combined with Artificial Intelligence (AI) will contribute to vehicle decision-making in the Intelligent Transportation System (ITS). Multi-vehicle pursuit (MVP) games, a multi-vehicle cooperative ability to capture mobile targets, are gradually becoming a hot research topic. Although there are some achievements in the field of MVP in the open space environment, the urban area brings complicated road structures and restricted moving spaces as challenges to the resolution of MVP games. We define an observation-constrained MVP (OMVP) problem in this paper and propose a transformer-based time and team reinforcement learning scheme (T3OMVP) to address the problem. First, a new multi-vehicle pursuit model is constructed based on Decentralized Partially Observed Markov Decision Processes (Dec-POMDPs) to instantiate this problem. Second, the QMIX is redefined to deal with the OMVP problem by leveraging the transformer-based observation sequence and combining the vehicle’s observations to reduce the influence of constrained observations. Third, a simulated urban environment is built to verify the proposed scheme. Extensive experimental results demonstrate that the proposed T3OMVP scheme achieves improvements relative to the state-of-the-art QMIX approaches by 9.66~106.25%, from simple to difficult scenarios. Full article
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17 pages, 5579 KiB  
Article
Transformer-Based Attention Network for Vehicle Re-Identification
Electronics 2022, 11(7), 1016; https://doi.org/10.3390/electronics11071016 - 24 Mar 2022
Cited by 13 | Viewed by 2268
Abstract
Vehicle re-identification (ReID) focuses on searching for images of the same vehicle across different cameras and can be considered as the most fine-grained ID-level classification task. It is fundamentally challenging due to the significant differences in appearance presented by a vehicle with the [...] Read more.
Vehicle re-identification (ReID) focuses on searching for images of the same vehicle across different cameras and can be considered as the most fine-grained ID-level classification task. It is fundamentally challenging due to the significant differences in appearance presented by a vehicle with the same ID (especially from different viewpoints) coupled with the subtle differences between vehicles with different IDs. Spatial attention mechanisms that have been proven to be effective in computer vision tasks also play an important role in vehicle ReID. However, they often require expensive key-point labels or suffer from noisy attention masks when trained without key-point labels. In this work, we propose a transformer-based attention network (TAN) for learning spatial attention information and hence for facilitating learning of discriminative features for vehicle ReID. Specifically, in contrast to previous studies that adopted a transformer network, we designed the attention network as an independent branch that can be flexibly utilized in various tasks. Moreover, we combined the TAN with two other branches: one to extract global features that define the image-level structures, and the other to extract the auxiliary side-attribute features that are invariant to viewpoint, such as color, car type, etc. To validate the proposed approach, experiments were conducted on two vehicle datasets (the VeRi-776 and VehicleID datasets) and a person dataset (Market-1501). The experimental results demonstrated that the proposed TAN is effective in improving the performance of both the vehicle and person ReID tasks, and the proposed method achieves state-of-the-art (SOTA) perfomance. Full article
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23 pages, 12436 KiB  
Article
A Smart Tourism Case Study: Classification of Accommodation Using Machine Learning Models Based on Accommodation Characteristics and Online Guest Reviews
Electronics 2022, 11(6), 913; https://doi.org/10.3390/electronics11060913 - 15 Mar 2022
Cited by 4 | Viewed by 2020
Abstract
This paper deals with the analysis of data retrieved from a web page for booking accommodation. The main idea of the research is to analyze the relationship between accommodation factors and customer reviews in order to determine the factors that have the greatest [...] Read more.
This paper deals with the analysis of data retrieved from a web page for booking accommodation. The main idea of the research is to analyze the relationship between accommodation factors and customer reviews in order to determine the factors that have the greatest influence on customer reviews. Machine learning methods are applied to the collected data and models that can predict the review category for those accommodations that are not evaluated by users are trained. The relationship between certain accommodation factors and classification accuracy of the models is examined in order to get detailed insight into the data used for model training, as well as to make the models more interpretable. The classification accuracy of each model is tested and the precision and recall of the models are examined and compared. Full article
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22 pages, 1798 KiB  
Article
A Corpus-Based Sentence Classifier for Entity–Relationship Modelling
Electronics 2022, 11(6), 889; https://doi.org/10.3390/electronics11060889 - 11 Mar 2022
Cited by 2 | Viewed by 1704
Abstract
Automated creation of a conceptual data model based on user requirements expressed in the textual form of a natural language is a challenging research area. The complexity of natural language requires deep insight into the semantics buried in words, expressions, and string patterns. [...] Read more.
Automated creation of a conceptual data model based on user requirements expressed in the textual form of a natural language is a challenging research area. The complexity of natural language requires deep insight into the semantics buried in words, expressions, and string patterns. For the purpose of natural language processing, we created a corpus of business descriptions and an adherent lexicon containing all the words in the corpus. Thus, it was possible to define rules for the automatic translation of business descriptions into the entity–relationship (ER) data model. However, since the translation rules could not always lead to accurate translations, we created an additional classification process layer—a classifier which assigns to each input sentence some of the defined ER method classes. The classifier represents a formalized knowledge of the four data modelling experts. This rule-based classification process is based on the extraction of ER information from a given sentence. After the detailed description, the classification process itself was evaluated and tested using the standard multiclass performance measures: recall, precision and accuracy. The accuracy in the learning phase was 96.77% and in the testing phase 95.79%. Full article
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Review

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14 pages, 1709 KiB  
Review
An Imperative Role of Digitalization in Monitoring Cattle Health for Sustainability
Electronics 2022, 11(17), 2702; https://doi.org/10.3390/electronics11172702 - 29 Aug 2022
Cited by 20 | Viewed by 2647
Abstract
In the current context, monitoring cattle health is critical for producing abundant milk to satisfy population growth demand and also for attaining sustainability. Traditional methods associated with cattle health must be strengthened in order to overcome the concern of detecting diseases based on [...] Read more.
In the current context, monitoring cattle health is critical for producing abundant milk to satisfy population growth demand and also for attaining sustainability. Traditional methods associated with cattle health must be strengthened in order to overcome the concern of detecting diseases based on the health condition. This problem has moved attention toward digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), cloud computing, edge/fog computing, big data, blockchain, drones, robotics, and augmented reality (AR)/virtual reality (VR), as these technologies have proved for real-time monitoring, intelligent analytics, secure data distribution, and real-time visual experience. The purpose of this study is to examine and discuss many cattle health disorders, as well as to address the fundamental notion of digital technologies, as well as the significance of these technologies for cattle health. Furthermore, the article addressed the different devices that integrated IoT and AI for cattle health monitoring, in which the previous architecture of cattle health monitoring is presented. Based on the review, the article discusses the challenges and suggests recommendations that can be implemented for the future work Full article
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