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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (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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| Paper Title: | Prediction of network traffic in wireless mesh networks using Hybrid Deep Learning Model |
| Authors Name: | R.JHANSI RAANI , A Vamsi , N Ashok Kumar Reddy , K Gowthami |
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IJNRD_207212
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| Published Paper Id: | IJNRD2310275 |
| Published In: | Volume 8 Issue 10, October-2023 |
| DOI: | |
| Abstract: | The exponential growth of wireless mesh networks (WMNs) has created a pressing need for efficient and accurate methods to predict network traffic. In this context, we present a novel approach that leverages a Hybrid Deep Learning Model (HDLM) to predict network traffic patterns in WMNs. Our hybrid model combines the strengths of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to capture both spatial and temporal dependencies in the network data. The proposed HDLM harnesses CNNs to extract spatial features from network topologies and traffic data while employing LSTM networks to model temporal dependencies over time. This fusion of spatial and temporal information enables our model to make accurate traffic predictions, making it well-suited for dynamic and evolving WMN environments. To validate the effectiveness of our approach, we conducted comprehensive experiments using real-world WMN datasets. The results demonstrate that the HDLM outperforms traditional prediction methods, achieving higher accuracy and robustness in traffic forecasting. Furthermore, the model exhibits adaptability to changing network conditions and provides valuable insights for network management and optimization. Our research contributes to the advancement of WMN management by offering a powerful prediction tool that enhances network resource allocation, quality of service (QoS) optimization, and proactive fault detection. The Hybrid Deep Learning Model promises to address the challenges of scalability and adaptability in WMNs, paving the way for more efficient and resilient wireless mesh network infrastructures. |
| Keywords: | Deep Learning Model, WMN management, Quality of Qervice, Fault Detection |
| Cite Article: | "Prediction of network traffic in wireless mesh networks using Hybrid Deep Learning Model ", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 10, page no.c685-c696, October-2023, Available :http://www.ijnrd.org/papers/IJNRD2310275.pdf |
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| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 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:IJNRD2310275 Registration ID: 207212 Published In: Volume 8 Issue 10, October-2023 DOI (Digital Object Identifier): Page No: c685-c696 Country: Chennai, TamilNadu, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2310275 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2310275 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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