Energy Aware Adaptive Sleep Scheduling and Secured Data Transmission Protocol to enhance QoS in IoT Networks using Improvised Firefly Bio-Inspired Algorithm (EAP-IFBA)

S Nithyanandh; S Omprakash; D Megala; M P Karthikeyan

doi:10.17485/IJST/v16i34.1706

Article

Energy Aware Adaptive Sleep Scheduling and Secured Data Transmission Protocol to enhance QoS in IoT Networks using Improvised Firefly Bio-Inspired Algorithm (EAP-IFBA)

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v16i34.1706

Year: 2023, Volume: 16, Issue: 34, Pages: 2753-2766

Original Article

Energy Aware Adaptive Sleep Scheduling and Secured Data Transmission Protocol to enhance QoS in IoT Networks using Improvised Firefly Bio-Inspired Algorithm (EAP-IFBA)

S Nithyanandh^1*, S Omprakash², D Megala³, M P Karthikeyan⁴

¹Assistant Professor & HOD, PG Department of Computer Science, Sankara College of Science and Commerce, Coimbatore, Tamil Nadu, India
²Assistant Professor, Department of Computer Science, KG College of Arts and Science, Coimbatore, Tamil Nadu, India
³Assistant Professor, Department of Information System Management, Gurunanak College, Chennai, Tamil Nadu, India
⁴Assistant Professor, School of CS & IT, Jain Deemed to be University, Bengaluru, Karnataka, India

*Corresponding Author
Email: [email protected]

Received Date:09 July 2023, Accepted Date:01 August 2023, Published Date:15 September 2023

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: To propose a suitable bio-inspired algorithm for energy-aware adaptive sleep scheduling and secured data transmission in IoT networks. Machine learning with bio-inspired technique is employed to schedule sleep periods for sensor nodes to maximize the lifetime of the IoT network, minimize energy consumption, and ensure robust data security during attacks. Methods: Improvised Firefly Bio-Inspired Algorithm (IFBA) is employed for adaptive sleep scheduling, and Dynamic Key Distribution Management (DKDM) with the elliptic curve method is used for secured and reliable data transmission between sensor nodes. Enhanced Recurrent Neural Networks (ERNN) with the N-Key method is deployed to identify the abnormal patterns associated with attacks and topology changes. Mean Square Error Data Recovery (MSEDR) is utilized to evaluate the error in data recovery, and Q-Learning Technique (QLT) with action sets is used to identify the finest path to ensure fast transmission of data. OMNETC++ simulator software is used to evaluate the performance of the proposed EAP-IFBA IoT network protocol with baseline protocols such as IWD-ARP, ECC-ILEACH, and RLSSACDGP. Findings: The proposed EAP-IBFA sleep scheduling and secured data transmission algorithm outperforms the prevailing methods IWD-ARP, ECCILEACH, and RLSSA-CDGP with an energy depletion rate of 8%, 97.5% alive nodes, 98% network life span in an IoT environment, 97.6% data transmission speed, 98% quick sleep scheduling, and 96.5% robustness to attacks. Novelty: The comprehensive solution of EAP-IFBA enhances QoS in IoT sensor networks. The proven results show that the proposed novel sleep scheduling and secureddata transmission algorithm has the ability to address the challenges of prevailing methods IWD-ARP, ECC-ILEACH, and RLSSA-CDGP in terms of energy consumption, data security, and dynamic sensing of topology changes for efficient and reliable IoT deployments.

Keywords: Energy Efficiency; IoT Networks; Sleep Scheduling; Secured Data Transmission; Machine Learning; Bio-Inspired Algorithm; Quality of Service

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Copyright

© 2023 Nithyanandh et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)