E-Learning Development Based on Internet of Things and Blockchain Technology during COVID-19 Pandemic

Rahmani, Amir Masoud; Malik, Mazhar Hussain; Malik, Tauqeer Safdar; Sadrishojaei, Mahyar; Hosseinzadeh, Mehdi; Al-Musawi, Ali S.

doi:10.3390/math9243151

Cited by 27 publications

(9 citation statements)

References 31 publications

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“…Consequently, many students engaged in some kind of remote education and are aware of the several advantages and possibilities offered by various teaching and learning methods [6]. With the aid of elearning concepts and digital technologies, students could continue their educational pursuits safely from a distance [41].…”

Section: Discussionmentioning

confidence: 99%

Internet of Things and Its Applications to Smart Campus: A Systematic Literature Review

Çavuş

Mrwebi

Ibrahim

et al. 2022

Int. J. Interact. Mob. Technol.

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A smart campus is an emerging trend that will revolutionize the education system by enabling universities to improve services, and processes as well as achieve sustainability goals. With the proliferation of advanced technologies, a smart campus has emerged as an important concept that integrates technology into higher education. A smart campus takes advantage of IoT technologies to facilitate teaching and research activities. The purpose of this study is to identify the IoT technologies that are required in the development of a smart campus. This study uses a Systematic Literature Review (SLR) methodology and PRISMA processes to analyze high-quality articles on the IoT-based smart campuses from the last five years (2017-2022) as extracted from three databases like Scopus, ScienceDirect, and IEEE. The findings of the study reveal that the implementation of an IoT-based smart campus offers many advantages and benefits but also presents challenges requiring further exploration. Because of the chosen research approach, the research results may lack generalizability. Therefore, researchers are encouraged to test the proposed propositions further. The paper explores the many benefits and advantages that are brought by the implementation of IoT-based smart campuses. It also identifies key challenges that are presented by such implementations. Researchers, policymakers, teachers, and students can benefit from this study by gaining insights into the IoT-based smart campus. Keywords—smart campus, smart university, Internet of Things, IoT applications, big data

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Section: Discussionmentioning

confidence: 99%

Internet of Things and Its Applications to Smart Campus: A Systematic Literature Review

Çavuş

Mrwebi

Ibrahim

et al. 2022

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

show abstract

“…The study's drawback is that it primarily addresses the application components of the subject matter, leaving out the physical components. The Internet of Things has advanced capabilities that make it possible to apply e-learning after an architecture has been developed, and the article [41] analyzes the current and projected state of the IoT world as it relates to the subject of education. Real-time measurements of students' skin resistance, brain waves, and pulse rates are carried out by a group of IoT sensors, which also include cameras, microphones, and wearable gadgets.…”

Section: Modern Trends Of Iot-based Teaching and Learningmentioning

confidence: 99%

Internet of Things (IoT)-Based Teaching and Learning: Modern Trends and Open Challenges

Ghashim,

Arshad

2023

Sustainability

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The effect of technology has caused many institutions to intend to change their approach to teaching and learning, resulting in the current model of teaching and learning being an active collaborative and self-directed one. The connection between education and technology has received attention as part of educational policy and practice. Debatable topics like how to use technology in the classroom have therefore punctuated complaints about declining educational standards, unemployed learners, duplicate curricula, and archaic institutional structure. In the framework of information and communication technologies and societal growth, the Internet of Things (IoT) is asserting its vital position. Institutions may improve learning outcomes with the use of the Internet of Things by offering enhanced learning experiences, increasing operational effectiveness, and collecting real-time, actionable insight into student performance. The current state of the Internet of Educational Things (IoET) is examined from several educational perspectives in this article; a few of them are highlighted, and both of their established and potential educational benefits are discussed. Additionally, it provides in-depth discussions on current issues and problems for the IoET from a variety of approaches. The investigation performs a bibliometric analysis with VOSviewer to focus on peer-reviewed research articles published in well-known publications databases over the past eight (8) years. The findings of this study should also act as a strong incentive for universities and colleges around the world to use IoT-based teaching and learning technology for improved academic achievement.

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“…The risk of fraudulent transactions is reduced through the identification of the most trusted nodes. Rahmani et al [23,24] presented an appropriate and optimal area coverage method that includes four phases. In the first phase, the overlap is calculated between the sensing range of sensor nodes in the network.…”

Section: Related Workmentioning

confidence: 99%

A Query-Based Greedy Approach for Authentic Influencer Discovery in SIoT

Batool¹,

Rehman²,

Kim³

et al. 2023

Computers, Materials &Amp; Continua

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The authors propose an informed search greedy approach that efficiently identifies the influencer nodes in the social Internet of Things with the ability to provide legitimate information. Primarily, the proposed approach minimizes the network size and eliminates undesirable connections. For that, the proposed approach ranks each of the nodes and prioritizes them to identify an authentic influencer. Therefore, the proposed approach discards the nodes having a rank (α) lesser than 0.5 to reduce the network complexity. α is the variable value represents the rank of each node that varies between 0 to 1. Node with the higher value of α gets the higher priority and vice versa. The threshold value α = 0.5 defined by the authors with respect to their network pruning requirements that can be vary with respect to other research problems. Finally, the algorithm in the proposed approach traverses the trimmed network to identify the authentic node to obtain the desired information. The performance of the proposed method is evaluated in terms of time complexity and accuracy by executing the algorithm on both the original and pruned networks. Experimental results show that the approach identifies authentic influencers on a resultant network in significantly less time than in the original network. Moreover, the accuracy of the proposed approach in identifying the influencer node is significantly higher than that of the original network. Furthermore, the comparison of the proposed approach with the existing approaches demonstrates its efficiency in terms of time consumption and network traversal through the minimum number of hops.

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E-Learning Development Based on Internet of Things and Blockchain Technology during COVID-19 Pandemic

Cited by 27 publications

References 31 publications

Internet of Things and Its Applications to Smart Campus: A Systematic Literature Review

Internet of Things and Its Applications to Smart Campus: A Systematic Literature Review

Internet of Things (IoT)-Based Teaching and Learning: Modern Trends and Open Challenges

A Query-Based Greedy Approach for Authentic Influencer Discovery in SIoT

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