Hybrid optimization for efficient 6G IoT traffic management and multi-routing strategy

Title

Hybrid optimization for efficient 6G IoT traffic management and multi-routing strategy

Creator

Logeshwaran J.; Patel S.K.; Kumar O.P.; Al-Zahrani F.A.

Description

Efficient traffic management solutions in 6G communication systems face challenges as the scale of the Internet of Things (IoT) grows. This paper aims to yield an all-inclusive framework ensuring reliable air pollution monitoring throughout smart cities, capitalizing on leading-edge techniques to encourage large coverage, high-accuracy data, and scalability. Dynamic sensors deployed to mobile ad-hoc pieces of fire networking sensors adapt to ambient changes. To address this issue, we proposed the Quantum-inspired Clustering Algorithm (QCA) and Quantum Entanglement and Mobility Metric (MoM) to enhance the efficiency and stability of clustering. Improved the sustainability and durability of the network by incorporating Dynamic CH selection employing Deep Reinforcement Learning (DRL). Data was successfully routed using a hybrid Quantum Genetic Algorithm and Ant Colony Optimization (QGA-ACO) approach. Simulation results were implemented using the ns-3 simulation tool, and the proposed model outperformed the traditional methods in deployment coverage (95%), cluster stability index (0.97), and CH selection efficiency (95%). This work is expected to study the 6G communication systems as a key enabler for IoT applications and as the title legible name explains, the solutions smartly done in a practical and scalable way gives a systematic approach towards solving the IoT traffic, and multi-routing challenges that are intended to be addressed in 6G era delivering a robust IoT ecosystem in securing the process. The Author(s) 2024.

Source

Scientific Reports, Vol-14, No. 1

Date

2024-01-01

Publisher

Nature Research

Subject

Air pollution monitoring; Deep reinforcement learning; Dynamic cluster head selection; IoT; Mobile ad-hoc networking; Quantum entanglement and mobility metric; Quantum genetic algorithm; Quantum-inspired clustering algorithm

Coverage

Logeshwaran J., Department of Computer Science, Christ University, Karnataka, Bengaluru, 560029, India; Patel S.K., Department of Computer Engineering, Marwadi University, Rajkot, 360003, India; Kumar O.P., Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India; Al-Zahrani F.A., Computer Engineering Department, Umm Al-Qura University, Mecca, 24381, Saudi Arabia

Rights

All Open Access; Gold Open Access

Relation

ISSN: 20452322; PubMed ID: 39730656

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.1038/s41598-024-81709-z

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213509382&doi=10.1038%2fs41598-024-81709-z&partnerID=40&md5=859b9bcf72dda30ad76c96981248813b