Prediction of network traffic in wireless mesh networks using Hybrid Deep Learning Model

Prediction of network traffic in wireless mesh networks using Hybrid Deep Learning Model

Registration ID: IJNRD_207212

Published ID: IJNRD2310275

DOI: Click Here to Get

R.JHANSI RAANI , A Vamsi , N Ashok Kumar Reddy , K Gowthami

Deep Learning Model, WMN management, Quality of Qervice, Fault Detection

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.

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"Prediction of network traffic in wireless mesh networks using Hybrid Deep Learning Model ", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 10, page no.c685-c696, October-2023, Available :https://ijnrd.org/papers/IJNRD2310275.pdf

Volume 8 Issue 10, October-2023

Pages : c685-c696

Paper Reg. ID: IJNRD_207212

Published Paper Id: IJNRD2310275

Downloads: 000121115

Research Area: Engineering

Country: Chennai, TamilNadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2310275.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2310275

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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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This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

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