Open Access

ARTICLE

A Tolerant and Energy Optimization Approach for Internet of Things to Enhance the QoS Using Adaptive Blended Marine Predators Algorithm

Vijaya Krishna Akula^1,*, Tan Kuan Tak², Pravin Ramdas Kshirsagar³, Shrikant Vijayrao Sonekar⁴, Gopichand Ginnela⁵

1 Department of Information Technology, G. Narayanamma Institute of Technology and Science for Women, Hyderabad, 500104, India
2 Engineering Cluster, Singapore Institute of Technology, 10 Dover Drive, Singapore, 138683, Singapore
3 Department of Electronics and Telecommunication Engineering, J.D. College of Engineering & Management, Nagpur, 441501, India
4 Department of Computer Science and Engineering, J.D. College of Engineering & Management, Nagpur, 441501, India
5 School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, 632014, Tamilnadu, India

* Corresponding Author: Vijaya Krishna Akula. Email:

Computers, Materials & Continua 2025, 83(2), 2449-2479. https://doi.org/10.32604/cmc.2025.061486

Received 26 November 2024; Accepted 12 February 2025; Issue published 16 April 2025

Abstract

The rapid expansion of Internet of Things (IoT) networks has introduced challenges in network management, primarily in maintaining energy efficiency and robust connectivity across an increasing array of devices. This paper introduces the Adaptive Blended Marine Predators Algorithm (AB-MPA), a novel optimization technique designed to enhance Quality of Service (QoS) in IoT systems by dynamically optimizing network configurations for improved energy efficiency and stability. Our results represent significant improvements in network performance metrics such as energy consumption, throughput, and operational stability, indicating that AB-MPA effectively addresses the pressing needs of modern IoT environments. Nodes are initiated with 100 J of stored energy, and energy is consumed at 0.01 J per square meter in each node to emphasize energy-efficient networks. The algorithm also provides sufficient network lifetime extension to a resourceful 7000 cycles for up to 200 nodes with a maximum Packet Delivery Ratio (PDR) of 99% and a robust network throughput of up to 1800 kbps in more compact node configurations. This study proposes a viable solution to a critical problem and opens avenues for further research into scalable network management for diverse applications.

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Keywords

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