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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
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Issue: April 2025
Volume 10 | Issue 4
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 10
Issue Published : 107
Article Submitted : 13636
Article Published : 6402
Total Authors : 16956
Total Reviewer : 634
Total Countries : 116
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| Paper Title: | Deep Learning-Driven Enhancement of IoT-Integrated Big Data Analytics in Edge-Cloud Architectures |
| Authors Name: | Priyanka Patani , Dr. Sanjay Gour |
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IJRTI_200950
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| Published Paper Id: | IJRTI2502106 |
| Published In: | Volume 10 Issue 2, February-2025 |
| DOI: | |
| Abstract: | This study demonstrates the potential of deep learning techniques for optimizing an IoT-enabled big data analytics architecture within an edge-cloud computing environment, focusing on anomaly detection. IoT systems generate huge amounts of data, presenting challenges related to real-time processing, network congestion, and security vulnerabilities. Traditional approaches often fall short due to latency, bandwidth limitations, and insufficient security measures. By leveraging deep learning models this framework captures temporal dependencies, identifies spatial patterns, and enhances classification accuracy, enabling real-time detection of anomalies and network attacks. The integration of edge computing further reduces network traffic and processing delays by moving computation closer to the data source, thus improving efficiency and privacy. This deep learning-driven approach not only enhances anomaly detection but also optimizes resource allocation, reduces latency, and minimizes energy consumption, providing a scalable and secure solution for IoT-enabled big data analytics across various applications, such as smart cities and industrial automation. |
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| Cite Article: | "Deep Learning-Driven Enhancement of IoT-Integrated Big Data Analytics in Edge-Cloud Architectures", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 2, page no.b40-b47, February-2025, Available :http://www.ijrti.org/papers/IJRTI2502106.pdf |
| Downloads: | 00075 |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2502106
Registration ID:200950
Published In: Volume 10 Issue 2, February-2025
DOI (Digital Object Identifier):
Page No: b40-b47 Country: Ahmedabad, Gujarat, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2502106 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2502106 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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