Open Access

ARTICLE

Deep Reinforcement Extreme Learning Machines for Secured Routing in Internet of Things (IoT) Applications

K. Lavanya^1,*, K. Vimala Devi², B. R. Tapas Bapu³

1 Velammal Engineering College, Chennai, 600066, India
2 Vellore Institute of Technology, Vellore, 632014, India
3 S A Engineering College, Chennai, 600077, India

* Corresponding Author: K. Lavanya. Email:

Intelligent Automation & Soft Computing 2022, 34(2), 837-848. https://doi.org/10.32604/iasc.2022.023055

Received 26 August 2021; Accepted 17 January 2022; Issue published 03 May 2022

Abstract

Multipath TCP (SMPTCP) has gained more attention as a valuable approach for IoT systems. SMPTCP is introduced as an evolution of Transmission Control Protocol (TCP) to pass packets simultaneously across several routes to completely exploit virtual networks on multi-homed consoles and other network services. The current multipath networking algorithms and simulation software strategies are confronted with sub-flow irregularity issues due to network heterogeneity, and routing configuration issues can be fixed adequately. To overcome the issues, this paper proposes a novel deep reinforcement-based extreme learning machines (DRLELM) approach to examine the complexities between routes, pathways, sub-flows, and SMPTCP connections in different topologies. Using DRLELM, throughput of the network is estimated. The extreme learning machines (ELM) preserves the run time wastage in multipath networks with faster convergence. Also, the Novel multipath TCP routing protocol integrates the logistic chaotic algorithm for the secured data transmission. Final results shows that the proposed framework outperformed other existing algorithms.

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Keywords

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