Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
Issues related to the use of IoT sensors are becoming more and more relevant in every significant public sphere. Undoubtedly, this includes the educational environment, especially during the distance learning, as during it a lot of new challenges and needs to students and teachers alike have emerged. Therefore, exploring the ways of using IoT sensors for successful implementation of learning activities and its regulation is quite a priority at the moment. Hence, the purpose of this study is to assess the impact of Internet of Things (IoT) technologies on enhancing the quality and effectiveness of distance learning in higher education institutions in Kazakhstan. The methods of this study involved conducting a systematic review of academic databases to identify and analyze peer-reviewed articles on the use of IoT sensors in enhancing student concentration during distance learning. It should be noted that the study was conducted within the framework of L.N. Gumilyov Eurasian National University, in particular, educational and professional activities of master’s degree students in Smart City specialty. Hence, the work obtained a set of results, important and priority for the development of educational environment. Accordingly, they are divided into both theoretical and practical aspects. In particular, the theoretical content and properties of such concepts as IoT sensors, pedagogical methods, and student concentration were studied in the paper. As for the practical part, it focuses more on the isolated parts of the general issue under study, in particular, the direct approaches and ways of using IoT sensors in order to enhance students’ concentration, have been established. The practical significance of the conducted research is revealed in the fact that its results can be used both as a scientific source for future research papers and as a methodological material during the development of curricula and programmes.
Issues related to the use of IoT sensors are becoming more and more relevant in every significant public sphere. Undoubtedly, this includes the educational environment, especially during the distance learning, as during it a lot of new challenges and needs to students and teachers alike have emerged. Therefore, exploring the ways of using IoT sensors for successful implementation of learning activities and its regulation is quite a priority at the moment. Hence, the purpose of this study is to assess the impact of Internet of Things (IoT) technologies on enhancing the quality and effectiveness of distance learning in higher education institutions in Kazakhstan. The methods of this study involved conducting a systematic review of academic databases to identify and analyze peer-reviewed articles on the use of IoT sensors in enhancing student concentration during distance learning. It should be noted that the study was conducted within the framework of L.N. Gumilyov Eurasian National University, in particular, educational and professional activities of master’s degree students in Smart City specialty. Hence, the work obtained a set of results, important and priority for the development of educational environment. Accordingly, they are divided into both theoretical and practical aspects. In particular, the theoretical content and properties of such concepts as IoT sensors, pedagogical methods, and student concentration were studied in the paper. As for the practical part, it focuses more on the isolated parts of the general issue under study, in particular, the direct approaches and ways of using IoT sensors in order to enhance students’ concentration, have been established. The practical significance of the conducted research is revealed in the fact that its results can be used both as a scientific source for future research papers and as a methodological material during the development of curricula and programmes.
The widespread adoption of fifth-generation mobile networks has spurred the rapid advancement of mobile edge computing (MEC). By decentralizing computing and storage resources to the network edge, MEC significantly enhances real-time data access services and enables efficient processing of large-scale dynamic data on resource-limited devices. However, MEC faces considerable security challenges, particularly in cross-domain service environments, where every device poses a potential security threat. To address this issue, this paper proposes a secure cross-domain authentication scheme based on a threshold signature tailored to MEC’s multi-subdomain nature. The proposed scheme employs a (t,n) threshold mechanism to bolster system resilience and security, catering to large-scale, dynamic, and decentralized MEC scenarios. Additionally, the proposed scheme features an efficient authorization update function that facilitates the revocation of malicious nodes. Security analysis confirmed that the proposed scheme satisfies unforgeability, collusion resistance, non-repudiation and forward security. Theoretical evaluation and experimental simulation verify the effectiveness and feasibility of the proposed scheme. Compared with existing schemes, the proposed scheme has higher computational performance while implementing secure authorization updates.
Today, assessing competition among college students in the job search is extremely important. However, various methods available are often inaccurate or inefficient when it comes to determining the level of their readiness for work. Conventional techniques usually depend on simplistic measures or miss out on crucial factors responsible for employability. The challenging characteristics of such competitive employment of college students are the lower levels of perceived stress, financing my education, and crucial professional skills. Hence, in this research, the Internet of Things Based on Binary Association Rule Extraction Algorithm (IoT-BAREA) technologies have improved college students' employment competitiveness. IoT-BAREA addresses this situation using a binary association rule extraction algorithm that helps detect significant patterns and relationships in large amounts of data involving student attributes and employment outcomes. IoT-BAREA positions itself as capable of providing insights into features that highly mediate the employability levels among students. This paper closes this gap and recommends a new IoT-BAREA method to help increase accuracy and efficiency in evaluating student employment competitiveness. Specifically, this study uses rigorous evaluation methods such as precision, recall and interaction ratio to determine how well IoT-BAREA predicts students' employability.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.