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Electronics
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IoT-Enabled Smart Applications for Post-COVID-19
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IoT-Enabled Smart Applications for Post-COVID-19

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

Deadline for manuscript submissions: 15 July 2024 | Viewed by 10348

Special Issue Editors

Dr. Mohammed Amin Almaiah

E-Mail Website
Guest Editor
Department of Computer Networks and Communications, King Faisal University, Al-Ahsa 31982, Saudi Arabia
Interests: mobile and wireless networks; cybersecurity and network security
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Nathir Obeidat

E-Mail Website
Guest Editor
Department of Internal Medicine, University of Jordan, Amman 11972, Jordan
Interests: smart medical systems; healthcare

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the role of IoT-enabled smart applications in shaping the future of our life after COVID-19. IoT technologies are opening new possibilities in different and heterogeneous fields, with remarkable applications being associated with the smart applications, continuously evolving and representing the future of smart cities. The main aim is to show how the research and education ecosystem promoting impactful solutions-oriented science related to IoT-enabled smart applications can help citizenry, government, industry, and other stakeholders to work collaboratively in order to make informed, socially responsible, science-based decisions. In addition, we aim to reveal the impact of IoT-enabled smart applications on the quality of our lives in different sectors such as smart healthcare, smart cities, smart education, smart government, and others. This Special Issue is aimed at highlighting the key inter-related challenges in areas such as the environment, climate change, mining, energy, agro-economy, water, and forestry that are limiting the development of a sustainable and resilient society with respect to the recent technological advances in IoT-based solutions. This Special Issue covers a wide range of topics on technology and innovation management related to sustainable societies and the internet of things within the scope of sustainability.

Dr. Mohammed Amin Almaiah
Prof. Dr. Nathir Obeidat
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 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

  • smart IoT for medical systems
  • sustainable IoT for sustainable society
  • environmental issues in IoT-based solutions
  • smart applications for education
  • smart applications for banking and e-commerce sector
  • internet of everything for sustainable and smart cities
  • security, privacy, and trust models for sustainable internet of things
  • 5G-empowered IoT solutions for sustainable development
  • artificial intelligence solutions for IoT-based smart and sustainable cities

Published Papers (3 papers)

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Research

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22 pages, 2041 KiB  
Article
Exploring the Potential of BERT-BiLSTM-CRF and the Attention Mechanism in Building a Tourism Knowledge Graph
by Hongsheng Xu, Ganglong Fan, Guofang Kuang and Chuqiao Wang
Electronics 2023, 12(4), 1010; https://doi.org/10.3390/electronics12041010 - 17 Feb 2023
Cited by 4 | Viewed by 2584
Abstract
As an important infrastructure in the era of big data, the knowledge graph can integrate and manage data resources. Therefore, the construction of tourism knowledge graphs with wide coverage and of high quality in terms of information from the perspective of tourists’ needs [...] Read more.
As an important infrastructure in the era of big data, the knowledge graph can integrate and manage data resources. Therefore, the construction of tourism knowledge graphs with wide coverage and of high quality in terms of information from the perspective of tourists’ needs is an effective solution to the problem of information clutter in the tourism field. This paper first analyzes the current state of domestic and international research on constructing tourism knowledge graphs and highlights the problems associated with constructing knowledge graphs, which are that they are time-consuming, laborious and have a single function. In order to make up for these shortcomings, this paper proposes a set of systematic methods to build a tourism knowledge graph. This method integrates the BiLSTM and BERT models and combines these with the attention mechanism. The steps of this methods are as follows: First, data preprocessing is carried out by word segmentation and removing stop words; second, after extracting the features and vectorization of the words, the cosine similarity method is used to classify the tourism text, with the text classification based on naive Bayes being compared through experiments; third, the popular tourism words are obtained through the popularity analysis model. This paper proposes two models to obtain popular words: One is a multi-dimensional tourism product popularity analysis model based on principal component analysis; the other is a popularity analysis model based on emotion analysis; fourth, this paper uses the BiLSTM-CRF model to identify entities and the cosine similarity method to predict the relationship between entities so as to extract high-quality tourism knowledge triplets. In order to improve the effect of entity recognition, this paper proposes entity recognition based on the BiLSTM-LPT and BiLSTM-Hanlp models. The experimental results show that the model can effectively improve the efficiency of entity recognition; finally, a high-quality tourism knowledge was imported into the Neo4j graphic database to build a tourism knowledge graph. Full article
(This article belongs to the Special Issue IoT-Enabled Smart Applications for Post-COVID-19)
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15 pages, 1642 KiB  
Article
A Secure Internet of Medical Things Framework for Breast Cancer Detection in Sustainable Smart Cities
by Theyazn H. H. Aldhyani, Mohammad Ayoub Khan, Mohammed Amin Almaiah, Noha Alnazzawi, Ahmad K. Al Hwaitat, Ahmed Elhag, Rami Taha Shehab and Ali Saleh Alshebami
Electronics 2023, 12(4), 858; https://doi.org/10.3390/electronics12040858 - 08 Feb 2023
Cited by 11 | Viewed by 2034
Abstract
Computational intelligence (CI) and artificial intelligence (AI) have incredible roles to play in the development of smart and sustainable healthcare systems by facilitating the integration of smart technologies with conventional medical procedures. The Internet of Things (IoT) and CI healthcare systems rely heavily [...] Read more.
Computational intelligence (CI) and artificial intelligence (AI) have incredible roles to play in the development of smart and sustainable healthcare systems by facilitating the integration of smart technologies with conventional medical procedures. The Internet of Things (IoT) and CI healthcare systems rely heavily on data collection and machine learning since miniature devices represent the foundation and paradigm shift to sustainable healthcare. With these advancements in AI techniques, we can reduce our environmental impact, while simultaneously enhancing the quality of our services. Widespread use of these devices for innovative IoT applications, however, generates massive amounts of data, which can significantly strain processing power. There is still a need for an efficient and sustainable model in the area of disease predictions, such as lung cancer, blood cancer, and breast cancer. The fundamental purpose of this research is to prove the efficacy of a secure Internet of Medical Things (IoMT) in the detection and management of breast cancer via the use of gated recurrent units (GRUs), which are a more recent version of recurrent neural networks (RNNs). The blockchain has been employed to achieve the secure IoMT. Unlike long short-term memory units, they do not have a cell state of their own. Therefore, we have combined GRU with RNN to achieve the best results. When training a GRU-RNN classifier, it is typically necessary to collect tagged IoT data from many sources, which raises significant concerns about the confidentiality of the data. To verify the model, the experiment is performed on Wisconsin Diagnostic Breast Cancer (WDBC). The experimental result shows that the GRU-RNN has been archived 95% in terms of the accuracy metric, and the efficacy of the proposed IoMT model is superior to the existing approach in terms of accuracy, precision, and recall. Full article
(This article belongs to the Special Issue IoT-Enabled Smart Applications for Post-COVID-19)
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Review

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19 pages, 2436 KiB  
Review
Cybersecurity Risk Analysis in the IoT: A Systematic Review
by Thanaa Saad AlSalem, Mohammed Amin Almaiah and Abdalwali Lutfi
Electronics 2023, 12(18), 3958; https://doi.org/10.3390/electronics12183958 - 20 Sep 2023
Cited by 2 | Viewed by 5095
Abstract
The Internet of Things (IoT) is increasingly becoming a part of our daily lives, raising significant concerns about future cybersecurity risks and the need for reliable solutions. This study conducts a comprehensive systematic literature review to examine the various challenges and attacks threatening [...] Read more.
The Internet of Things (IoT) is increasingly becoming a part of our daily lives, raising significant concerns about future cybersecurity risks and the need for reliable solutions. This study conducts a comprehensive systematic literature review to examine the various challenges and attacks threatening IoT cybersecurity, as well as the proposed frameworks and solutions. Furthermore, it explores emerging trends and identifies existing gaps in this domain. The study’s novelty lies in its extensive exploration of machine learning techniques for detecting and countering IoT threats. It also contributes by highlighting research gaps in economic impact assessment and industrial IoT security. The systematic review analyzes 40 articles, providing valuable insights and guiding future research directions. Results show that privacy issues and cybercrimes are the primary concerns in IoT security, and artificial intelligence holds promise for future cybersecurity. However, some attacks remain inadequately addressed by existing solutions, such as confidentiality, security authentication, and data server connection attacks, necessitating further research and real-life testing of proposed remedies. Full article
(This article belongs to the Special Issue IoT-Enabled Smart Applications for Post-COVID-19)
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