ENGINEERING SOLUTIONS FOR THE INTEGRATION OF INTERNET OF THINGS DEVICES AND SENSORS

The integration of devices and sensors within the Internet of Things (IoT) expands not only the scope for monitoring and controlling environments but also fundamentally redefines the management of data, with promising implications across various sectors. This paper explores a novel approach to IoT data processing that leverages edge-based neural networks within sensor networks, enabling on-site data analysis and decision-making. Our study focuses on sectors with high latency sensitivity—such as remote healthcare, predictive maintenance in industrial settings, and real-time environmental monitoring. By embedding advanced data analytics closer to the source, this approach enhances data privacy, reduces communication loads, and paves the way for more resilient and scalable IoT ecosystems. We address both the technological benefits and implementation challenges, offering insights into how these advancements can transform industries through increased data autonomy and efficiency. References 21, tables 2, figures 5.

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Publication Details

Journal
Tekhnichna Elektrodynamika
Published
2025-09-04
DOI
https://doi.org/10.15407/techned2025.05.089
Citations
1
Primary Topic
Distributed and Parallel Computing Systems
Type
article
Field-Weighted Citation Impact
1.49
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article

ENGINEERING SOLUTIONS FOR THE INTEGRATION OF INTERNET OF THINGS DEVICES AND SENSORS

Rzayeva Sona Vaqif, G.S. Kerimzade
1 citations
Tekhnichna Elektrodynamika
Distributed and Parallel Computing Systems
1.49
article

ENGINEERING SOLUTIONS FOR THE INTEGRATION OF INTERNET OF THINGS DEVICES AND SENSORS

Rzayeva Sona Vaqif, G.S. Kerimzade
article en
1 citations

Abstract

The integration of devices and sensors within the Internet of Things (IoT) expands not only the scope for monitoring and controlling environments but also fundamentally redefines the management of data, with promising implications across various sectors. This paper explores a novel approach to IoT data processing that leverages edge-based neural networks within sensor networks, enabling on-site data analysis and decision-making. Our study focuses on sectors with high latency sensitivity—such as remote healthcare, predictive maintenance in industrial settings, and real-time environmental monitoring. By embedding advanced data analytics closer to the source, this approach enhances data privacy, reduces communication loads, and paves the way for more resilient and scalable IoT ecosystems. We address both the technological benefits and implementation challenges, offering insights into how these advancements can transform industries through increased data autonomy and efficiency. References 21, tables 2, figures 5.

Tekhnichna ElektrodynamikaVol. 2025(5)
Azerbaijan State Oil and Industry University (AZ)
Openalex Percentile: Top 13%
Distributed and Parallel Computing Systems
1.49
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