Special Issue "Recent Trends and Applications of Blockchain and IoT Technologies to Combat COVID-19"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 December 2021) | Viewed by 4251

Special Issue Editors

Dr. Asadullah Shaikh
E-Mail Website
Guest Editor
College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
Interests: model verification; Service Oriented Architecture (SOA); Model Driven Development (MDD)
Prof. Dr. Uffe Kock Wiil
E-Mail Website
Guest Editor
Maersk Mc Kinney Moller Institute, University of Sothern Denmark, Campusvej 55, 5230 Odense M, Denmark
Interests: health informatics; security informatics; social network analysis and mining; hypermedia; data-driven health technology
Dr. Yousef Asiri
E-Mail Website
Guest Editor
College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
Interests: human–computer interaction; machine learning; data analytics

Special Issue Information

Dear Colleagues,

The special issue invites high-quality original research papers and reviews in the area of Blockchain, security and IoT technologies to minimize the impact of COVID-19. Articles should focus on the application of Blockchain and IoT as enabling technologies to combat various societal challenges posed by COVID-19.

The blend of Blockchain and IoT has recently received much attention. Such enabling technologies have the ability to collect, put together, safeguard, store, communicate, and control data to help manage the COVID-19 pandemic. The special issue will focus on recent trends and applications of Blockchain and IoT technologies that have a direct impact on various areas in society affected by COVID-19.

Topics to be discussed in this special issue include (but are not limited to) the following:

  • Blockchain technology for IoT
  • Blockchain AI-based IoT protocols
  • Sensors and distributed ledger
  • Security and privacy in IoT applications
  • Blockchain architecture in IoT security
  • Decentralization for Blockchain in IoT devices
  • Blockchain for IoT-enabled fields (i.e., health, energy, transport, industry)

Dr. Asadullah Shaikh
Prof. Dr. Uffe Kock Wiil
Dr. Yousef Asiri
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. 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 2300 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 (4 papers)

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Research

Article
A WSN Framework for Privacy Aware Indoor Location
Appl. Sci. 2022, 12(6), 3204; https://doi.org/10.3390/app12063204 - 21 Mar 2022
Viewed by 419
Abstract
In the past two decades, technological advancements in smart devices, IoT, and smart sensors have paved the way towards numerous implementations of indoor location systems. Indoor location has many important applications in numerous fields, including structural engineering, behavioral studies, health monitoring, etc. However, [...] Read more.
In the past two decades, technological advancements in smart devices, IoT, and smart sensors have paved the way towards numerous implementations of indoor location systems. Indoor location has many important applications in numerous fields, including structural engineering, behavioral studies, health monitoring, etc. However, with the recent COVID-19 pandemic, indoor location systems have gained considerable attention for detecting violations in physical distancing requirements and monitoring restrictions on occupant capacity. However, existing systems that rely on wearable devices, cameras, or sound signal analysis are intrusive and often violate privacy. In this research, we propose a new framework for indoor location. We present an innovative, non-intrusive implementation of indoor location based on wireless sensor networks. Further, we introduce a new protocol for querying and performing computations in wireless sensor networks (WSNs) that preserves sensor network anonymity and obfuscates computation by using onion routing. We also consider the single point of failure (SPOF) of sink nodes in WSNs and substitute them with a blockchain-based application through smart contracts. Our set of smart contracts is able to build the onion data structure and store the results of computation. Finally, a role-based access control contract is used to secure access to the system. Full article
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Article
Implementation of a Blockchain System Using Improved Elliptic Curve Cryptography Algorithm for the Performance Assessment of the Students in the E-Learning Platform
Appl. Sci. 2022, 12(1), 74; https://doi.org/10.3390/app12010074 - 22 Dec 2021
Cited by 1 | Viewed by 884
Abstract
Blockchain technology allows for the decentralized creation of a propagated record of digital events, in which third parties do not control information and associated transactions. This methodology was initially developed for value transmission. Still, it now has a broad array of utilization in [...] Read more.
Blockchain technology allows for the decentralized creation of a propagated record of digital events, in which third parties do not control information and associated transactions. This methodology was initially developed for value transmission. Still, it now has a broad array of utilization in various industries, including health, banking, the internet of things, and several others. With its numerous added benefits, a blockchain-based learning management system is a commonly utilized methodology at academic institutes, and more specifically during and after the COVID-19 period. It also presents several potentials for decentralized, interoperable record management in the academic system in education. Integrity, authenticity, and peer-executed smart contracts (SC) are some of the qualities of a blockchain that could introduce a new degree of safety, trustworthiness, and openness to e-learning. This research proposes a unique encryption technique for implementing a blockchain system in an e-learning (EL) environment to promote transparency in assessment procedures. Our methodology may automate evaluations and provide credentials. We built it to be analytical and content-neutral in order to demonstrate the advantages of a blockchain back-end to end-users, including student and faculty members particularly during this COVID-19 era. This article explains the employment of blockchain and SC in e-learning. To improve the trust in the assessment, we propose a novel improved elliptic curve cryptography algorithm (IECCA) for data encryption and decryption. The performance of the suggested method is examined by comparing it with various existing algorithms of encryption. The evaluation of the behaviour of the presented method demonstrates that the technique shall enhance trust in online educational systems, assessment processes, educational history, and credentials. Full article
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Article
SPIN: A Blockchain-Based Framework for Sharing COVID-19 Pandemic Information across Nations
Appl. Sci. 2021, 11(18), 8767; https://doi.org/10.3390/app11188767 - 21 Sep 2021
Cited by 2 | Viewed by 677
Abstract
The COVID-19 pandemic has caused many countries around the globe to put strict policies and measures in place in an attempt to control the rapid spread of the virus. These measures have affected economic activities and have impacted a broad range of businesses, [...] Read more.
The COVID-19 pandemic has caused many countries around the globe to put strict policies and measures in place in an attempt to control the rapid spread of the virus. These measures have affected economic activities and have impacted a broad range of businesses, such as international traveling, restaurants, and shopping malls. As COVID-19 vaccination efforts progress, countries are starting to relax international travel constraints and permit passengers from certain destinations to cross the border. Moreover, travelers from those destinations are likely required to provide certificates of vaccination results or negative COVID-19 tests before crossing the borders. Implementing these travel guidelines requires sharing information between countries, such as the number of COVID-19 cases and vaccination certificates for travelers. In this paper, we introduce SPIN, a framework leveraging a permissioned blockchain for sharing COVID-19 information between countries. This includes public data, such as the number of vaccinated people, and private data, such as vaccination certificates for individuals. Additionally, we employ cancelable fingerprint templates to authenticate private information about travelers. We analyze the framework from scalability, efficiency, security, and privacy perspectives. To validate our framework, we provide a prototype implementation using the Hyperledger Fabric platform. Full article
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Article
A Novel Hybrid Deep Learning Model for Detecting COVID-19-Related Rumors on Social Media Based on LSTM and Concatenated Parallel CNNs
Appl. Sci. 2021, 11(17), 7940; https://doi.org/10.3390/app11177940 - 28 Aug 2021
Cited by 4 | Viewed by 871
Abstract
Spreading rumors in social media is considered under cybercrimes that affect people, societies, and governments. For instance, some criminals create rumors and send them on the internet, then other people help them to spread it. Spreading rumors can be an example of cyber [...] Read more.
Spreading rumors in social media is considered under cybercrimes that affect people, societies, and governments. For instance, some criminals create rumors and send them on the internet, then other people help them to spread it. Spreading rumors can be an example of cyber abuse, where rumors or lies about the victim are posted on the internet to send threatening messages or to share the victim’s personal information. During pandemics, a large amount of rumors spreads on social media very fast, which have dramatic effects on people’s health. Detecting these rumors manually by the authorities is very difficult in these open platforms. Therefore, several researchers conducted studies on utilizing intelligent methods for detecting such rumors. The detection methods can be classified mainly into machine learning-based and deep learning-based methods. The deep learning methods have comparative advantages against machine learning ones as they do not require preprocessing and feature engineering processes and their performance showed superior enhancements in many fields. Therefore, this paper aims to propose a Novel Hybrid Deep Learning Model for Detecting COVID-19-related Rumors on Social Media (LSTM–PCNN). The proposed model is based on a Long Short-Term Memory (LSTM) and Concatenated Parallel Convolutional Neural Networks (PCNN). The experiments were conducted on an ArCOV-19 dataset that included 3157 tweets; 1480 of them were rumors (46.87%) and 1677 tweets were non-rumors (53.12%). The findings of the proposed model showed a superior performance compared to other methods in terms of accuracy, recall, precision, and F-score. Full article
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