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Context Aware Hybrid Network Architecture for Iot with Machine Learning Based Intelligent Gateway

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Abstract

The substantial growth of Internet of Things (IoT) over the past few decades has drawn the attention of academia and research. The growing technology is faced with challenges like limited bandwidth and power. The low power and lossy IoT networks mainly depend on wireless protocols for communication and connectivity. Wireless spectrum, whether licensed or unlicensed, is limited and already crowded. To address the issue of limited wireless spectrum, integration of wired and wireless techniques has been proposed by researchers. However, state-of-art solutions mainly focus on partially wired and wireless networks. Seamless integration of these techniques needs to be explored at front end due to voluminous IoT traffic. The paper presents an intelligent gateway to support hybridization of wired and wireless networks at front end. The proposed gateway utilizes machine learning algorithm for classifying the incoming user tasks and selecting a particular link (wired or wireless). k-Nearest Neighbor (kNN), Decision tree (DT), Support Vector Machine (SVM) and Naive Bayes (NB) have been compared for performance using MATLABR2015a. Furthermore, performance analysis of the proposed architecture has been done for reliability, Bit Error Rate (BER), effective throughput and cost. The results prove the efficacy of hybrid architecture as compared to completely wired and wireless solutions.

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Acknowledgements

We want to thank the Department of Science and Technology (DST), Govt. of India, for supporting the project grant no. SR/WOS-A/ET-8/2018(G)&(C) and Gautam Buddha University to provide facilities and support for completing this research work.

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  1. School of Information and Communication Technology, Gautam Buddha University, Greater Noida, Uttar Pradesh, 201308, India

    Neeti Gupta & Vidushi Sharma

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Gupta, N., Sharma, V. Context Aware Hybrid Network Architecture for Iot with Machine Learning Based Intelligent Gateway. SN COMPUT. SCI. 4, 297 (2023). https://doi.org/10.1007/s42979-023-01736-x

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