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Artificial Intelligence and Edge Computing in IoT-Based Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 15 August 2025 | Viewed by 1979

Special Issue Editor

School of Information and Communication Engineering, Beijing University of Posts and Telecommunication,100876 Beijing, China
Interests: IoT; future network; TSN; edge computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of edge computing along with artificial intelligence technology has great potential to gather and process large amounts of IoT data, maximize the potential for rapid, real-time data analysis and intelligent decision-making, and deliver a variety of low-latency, reliable, intelligent, and time-sensitive services.

This Special Issue aims to focus on all aspects of research relevant to artificial intelligence and edge computing technologies in IoT applications. This Special Issue invites research and review papers that report on novel contributions with respect to cloud–edge collaboration, federated learning, the utilization of a large language model (LLM), and related innovative applications within the IoT.

Dr. Fangmin Xu
Guest Editor

Manuscript Submission Information

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Keywords

  • Internet of Things
  • artificial intelligence
  • network architecture
  • resource management
  • cyber security
  • edge computing

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Published Papers (1 paper)

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Research

16 pages, 1124 KiB  
Article
Hybrid CNN-GRU Model for Real-Time Blood Glucose Forecasting: Enhancing IoT-Based Diabetes Management with AI
by Reem Ibrahim Alkanhel, Hager Saleh, Ahmed Elaraby, Saleh Alharbi, Hela Elmannai, Saad Alaklabi, Saeed Hamood Alsamhi and Sherif Mostafa
Sensors 2024, 24(23), 7670; https://doi.org/10.3390/s24237670 - 30 Nov 2024
Viewed by 1705
Abstract
For people with diabetes, controlling blood glucose level (BGL) is a significant issue since the disease affects how the body metabolizes food, which makes careful insulin regulation necessary. Patients have to manually check their blood sugar levels, which can be laborious and inaccurate. [...] Read more.
For people with diabetes, controlling blood glucose level (BGL) is a significant issue since the disease affects how the body metabolizes food, which makes careful insulin regulation necessary. Patients have to manually check their blood sugar levels, which can be laborious and inaccurate. Many variables affect BGL changes, making accurate prediction challenging. To anticipate BGL many steps ahead, we propose a novel hybrid deep learning model framework based on Gated Recurrent Units (GRUs) and Convolutional Neural Networks (CNNs), which can be integrated into the Internet of Things (IoT)-enabled diabetes management systems, improving prediction accuracy and timeliness by allowing real-time data processing on edge devices. While the GRU layer records temporal relationships and sequence information, the CNN layer analyzes the incoming data to extract significant features. Using a publicly accessible type 1 diabetes dataset, the hybrid model’s performance is compared to that of the standalone Long Short-Term Memory (LSTM), CNN, and GRU models. The findings show that the hybrid CNN-GRU model performs better than the single models, indicating its potential to significantly improve real-time BGL forecasting in IoT-based diabetes management systems. Full article
(This article belongs to the Special Issue Artificial Intelligence and Edge Computing in IoT-Based Applications)
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