Hybrid optimization for efficient 6G IoT traffic management and multi-routing strategy
Abstract 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 techniq...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-81709-z |
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| author | J. Logeshwaran Shobhit K. Patel Om Prakash Kumar Fahah Ahmed Al-Zahrani |
| author_facet | J. Logeshwaran Shobhit K. Patel Om Prakash Kumar Fahah Ahmed Al-Zahrani |
| author_sort | J. Logeshwaran |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-4dee208980e249a9ad963f5e0e4c97e9 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-4dee208980e249a9ad963f5e0e4c97e92024-12-29T12:16:31ZengNature PortfolioScientific Reports2045-23222024-12-0114111710.1038/s41598-024-81709-zHybrid optimization for efficient 6G IoT traffic management and multi-routing strategyJ. Logeshwaran0Shobhit K. Patel1Om Prakash Kumar2Fahah Ahmed Al-Zahrani3Department of Computer Science, Christ UniversityDepartment of Computer Engineering, Marwadi UniversityDepartment of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher EducationComputer Engineering Department, Umm Al-Qura UniversityAbstract 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.https://doi.org/10.1038/s41598-024-81709-zAir pollution monitoringIoTMobile ad-hoc networkingQuantum-inspired clustering algorithmQuantum entanglement and mobility metricDeep reinforcement learning |
| spellingShingle | J. Logeshwaran Shobhit K. Patel Om Prakash Kumar Fahah Ahmed Al-Zahrani Hybrid optimization for efficient 6G IoT traffic management and multi-routing strategy Scientific Reports Air pollution monitoring IoT Mobile ad-hoc networking Quantum-inspired clustering algorithm Quantum entanglement and mobility metric Deep reinforcement learning |
| title | Hybrid optimization for efficient 6G IoT traffic management and multi-routing strategy |
| title_full | Hybrid optimization for efficient 6G IoT traffic management and multi-routing strategy |
| title_fullStr | Hybrid optimization for efficient 6G IoT traffic management and multi-routing strategy |
| title_full_unstemmed | Hybrid optimization for efficient 6G IoT traffic management and multi-routing strategy |
| title_short | Hybrid optimization for efficient 6G IoT traffic management and multi-routing strategy |
| title_sort | hybrid optimization for efficient 6g iot traffic management and multi routing strategy |
| topic | Air pollution monitoring IoT Mobile ad-hoc networking Quantum-inspired clustering algorithm Quantum entanglement and mobility metric Deep reinforcement learning |
| url | https://doi.org/10.1038/s41598-024-81709-z |
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