IoT Adoption Model for E-Learning in Higher Education Institutes: A Case Study in Saudi Arabia

Sustainability, Journal Year: 2023, Volume and Issue: 15(12), P. 9748 - 9748

Published: June 19, 2023

The realm of the Internet Things (IoT), while continually transforming as a novel paradigm in nexus technology and education, still contends with numerous obstacles that hinder its incorporation into higher education institutions’ (HEIs) e-learning platforms. Despite substantial strides IoT utilization from industrialized nations—the United States, Kingdom, Japan, China serving prime exemplars—the scope implementation developing countries, notably Saudi Arabia, Malaysia, Pakistan, Bangladesh, lags behind. A significant gap exists research centered on trajectory integration within systems economically disadvantaged nations. Specifically, this study centers Arabia to illuminate main factors catalyzing or encumbering uptake HEIs’ sector. As preliminary step, has embarked an exhaustive dissection prior studies unearth critical variables implicated adoption process. Subsequently, we employed inferential methodology, amassing data 384 respondents Arabian HEIs. Our examination divulges usability, accessibility, technical support, individual proficiencies considerably contribute rate incorporation. Furthermore, our infer financial obstacles, self-efficacy, interactive capability, online surveillance, automated attendance tracking, training programs, network safeguarding measures, relevant tools significantly influence adoption. Contrarily, such internet quality, infrastructure preparedness, privacy concerns, faculty support appeared have negligible impact rates This culminates offering concrete recommendations bolster HEIs, presenting valuable insights for government entities, policy architects, HEIs address hurdles associated

Language: Английский

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Privacy-Preserving Big Data Security for IoT With Federated Learning and Cryptography

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 120918 - 120934

Published: Jan. 1, 2023

In the ever-expanding Internet of Things (IoT) domain, production data has reached an unparalleled scale. This massive is processed to glean invaluable insights, accelerating a myriad decision-making processes. Nevertheless, privacy and security such information present formidable challenges. study proposes innovative methodology for resolving these challenges, by augmenting efficacy big analytics through federated learning in IoT ecosystem. The proffered approach amalgamates hierarchical structure, scalable rate, rudimentary cryptographic mechanism foster while ensuring robust security. Additionally, we introduce novel communication protocol - SEPP-IoT, designed facilitate efficient, secure, confidential interactions between devices central server. our pursuit optimizing overhead, propose adaptive compression algorithm, aimed at curbing volume transferred To fortify resilience fault tolerance, incorporates multiple mechanisms as replication, error correction codes, proactive detection recovery. Trust management, salient feature framework, bolsters integrity learning. We recommend unique technique that gauges dependability nodes using four trust parameters. employ FedSim simulator evaluate method's effectiveness. results indicate notable enhancement efficiency within IoT.

Language: Английский

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A Novel Deep Learning Approach for Real-Time Critical Assessment in Smart Urban Infrastructure Systems

Electronics, Journal Year: 2024, Volume and Issue: 13(16), P. 3286 - 3286

Published: Aug. 19, 2024

The swift advancement of communication and information technologies has transformed urban infrastructures into smart cities. Traditional assessment methods face challenges in capturing the complex interdependencies temporal dynamics inherent these systems, risking resilience. This study aims to enhance criticality geographic zones within cities by introducing a novel deep learning architecture. Utilizing Convolutional Neural Networks (CNNs) for spatial feature extraction Long Short-Term Memory (LSTM) networks dependency modeling, proposed framework processes inputs such as total electricity use, flooding levels, population, poverty rates, energy consumption. CNN component constructs hierarchical maps through successive convolution pooling operations, while LSTM captures sequence-based patterns. Fully connected layers integrate features generate final predictions. Implemented Python using TensorFlow Keras on an Intel Core i7 system with 32 GB RAM NVIDIA GTX 1080 Ti GPU, model demonstrated superior performance. It achieved mean absolute error 0.042, root square 0.067, R-squared value 0.935, outperforming existing methodologies real-time adaptability resource efficiency.

Language: Английский

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Secure trust aware multi-objective routing protocol based on battle competitive swarm optimization in IoT

Artificial Intelligence Review, Journal Year: 2023, Volume and Issue: 56(S2), P. 1685 - 1709

Published: Oct. 11, 2023

Language: Английский

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Use of the Swarm Control Method to Maintain the Homeostasis of a Complex System Based on Interacting Information Objects

Automatic Documentation and Mathematical Linguistics, Journal Year: 2024, Volume and Issue: 58(5), P. 310 - 319

Published: Oct. 1, 2024

Language: Английский

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Introduction to the special section on Computing and Communication Networks (ICCCN 2022) (VSI-icccn)

Physical Communication, Journal Year: 2023, Volume and Issue: 60, P. 102152 - 102152

Published: July 13, 2023

Language: Английский

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