Hybrid Feed Forward Neural Networks and Particle Swarm Optimization for Intelligent Self-Organization in the Industrial Communication Networks
In recent times, the Industrial Communication Networks (ICNets) have been playing a vital role in advancing mobile generation networks, especially in the evolution of 6G networks. This research proposes a novel technique for self-organization that integrates Feed Forward Neural Network (FFNN) and Pa...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of the Communications Society |
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| Online Access: | https://ieeexplore.ieee.org/document/10960282/ |
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| author | M. Maragatharajan Aanjankumar Sureshkumar Rajesh Kumar Dhanaraj E. Nirmala Md Shohel Sayeed Mohammad Tabrez Quasim Shakila Basheer |
| author_facet | M. Maragatharajan Aanjankumar Sureshkumar Rajesh Kumar Dhanaraj E. Nirmala Md Shohel Sayeed Mohammad Tabrez Quasim Shakila Basheer |
| author_sort | M. Maragatharajan |
| collection | DOAJ |
| description | In recent times, the Industrial Communication Networks (ICNets) have been playing a vital role in advancing mobile generation networks, especially in the evolution of 6G networks. This research proposes a novel technique for self-organization that integrates Feed Forward Neural Network (FFNN) and Particle Swarm Optimization (PSO) to enhance the network management, optimization and adaptive learning in 6G. The traditional self-organization models in ICNets and 6G depends on rule-based heuristic, reinforcement learning and classical optimization techniques, which often struggle with high computational complexity, slow convergence rates, and suboptimal decision making. In contrast, FFNN+PSO fusion model leverages the predictive learning capability of FFNN and the global optimization strength of PSO to ensure intelligent self-optimization, real-time adaptability, and ultra-low-latency in the dynamically changing 6G environments. The experimental results demonstrate that the proposed method achieves a significantly higher accuracy of 98.25% by outperforming the existing models such as Random Forest (80%), Reinforcement learning (90%), Max Overlapping (88%), and Ant Colony Optimization (92%), Further, the proposed method enhances the energy efficiency, complex network function approximation, and collaborative optimization which make it an ideal choice for scalable and self-organization model in the 6G and ICNets. This study provides a transformative contribution to self-organization in the 6G networks and it offers robust, high-performance alternative to the conventional models as well it ensures massive device connectivity with intelligent network adaptation. |
| format | Article |
| id | doaj-art-247bd6c19a2d4a08a2f92ca1d03288d7 |
| institution | Kabale University |
| issn | 2644-125X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Communications Society |
| spelling | doaj-art-247bd6c19a2d4a08a2f92ca1d03288d72025-08-20T03:52:51ZengIEEEIEEE Open Journal of the Communications Society2644-125X2025-01-0163816383310.1109/OJCOMS.2025.355917210960282Hybrid Feed Forward Neural Networks and Particle Swarm Optimization for Intelligent Self-Organization in the Industrial Communication NetworksM. Maragatharajan0https://orcid.org/0000-0002-7226-5644Aanjankumar Sureshkumar1https://orcid.org/0000-0002-3065-0624Rajesh Kumar Dhanaraj2https://orcid.org/0000-0002-2038-7359E. Nirmala3https://orcid.org/0000-0001-6423-7195Md Shohel Sayeed4https://orcid.org/0000-0002-0052-4870Mohammad Tabrez Quasim5https://orcid.org/0000-0002-5546-0405Shakila Basheer6https://orcid.org/0000-0001-9032-9560School of Computing Science and Engineering, VIT Bhopal University, Sehore, IndiaSymbiosis Institute of Computer Studies and Research, Symbiosis International (Deemed University), Pune, IndiaSymbiosis Institute of Computer Studies and Research, Symbiosis International (Deemed University), Pune, IndiaDepartment of Computer Science and Artificial Intelligence, College of Computing and Information Technology, University of Bisha, Bisha, Saudi ArabiaFaculty of Information Science and Technology, Multimedia University, Melaka, MalaysiaDepartment of Computer Science and Artificial Intelligence, College of Computing and Information Technology, University of Bisha, Bisha, Saudi ArabiaDepartment of Information Systems, College of computer and Information science, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaIn recent times, the Industrial Communication Networks (ICNets) have been playing a vital role in advancing mobile generation networks, especially in the evolution of 6G networks. This research proposes a novel technique for self-organization that integrates Feed Forward Neural Network (FFNN) and Particle Swarm Optimization (PSO) to enhance the network management, optimization and adaptive learning in 6G. The traditional self-organization models in ICNets and 6G depends on rule-based heuristic, reinforcement learning and classical optimization techniques, which often struggle with high computational complexity, slow convergence rates, and suboptimal decision making. In contrast, FFNN+PSO fusion model leverages the predictive learning capability of FFNN and the global optimization strength of PSO to ensure intelligent self-optimization, real-time adaptability, and ultra-low-latency in the dynamically changing 6G environments. The experimental results demonstrate that the proposed method achieves a significantly higher accuracy of 98.25% by outperforming the existing models such as Random Forest (80%), Reinforcement learning (90%), Max Overlapping (88%), and Ant Colony Optimization (92%), Further, the proposed method enhances the energy efficiency, complex network function approximation, and collaborative optimization which make it an ideal choice for scalable and self-organization model in the 6G and ICNets. This study provides a transformative contribution to self-organization in the 6G networks and it offers robust, high-performance alternative to the conventional models as well it ensures massive device connectivity with intelligent network adaptation.https://ieeexplore.ieee.org/document/10960282/Self-organizationparticle swarm optimizationfeed forward neural networksactivation functionbandwidthfitness evaluation |
| spellingShingle | M. Maragatharajan Aanjankumar Sureshkumar Rajesh Kumar Dhanaraj E. Nirmala Md Shohel Sayeed Mohammad Tabrez Quasim Shakila Basheer Hybrid Feed Forward Neural Networks and Particle Swarm Optimization for Intelligent Self-Organization in the Industrial Communication Networks IEEE Open Journal of the Communications Society Self-organization particle swarm optimization feed forward neural networks activation function bandwidth fitness evaluation |
| title | Hybrid Feed Forward Neural Networks and Particle Swarm Optimization for Intelligent Self-Organization in the Industrial Communication Networks |
| title_full | Hybrid Feed Forward Neural Networks and Particle Swarm Optimization for Intelligent Self-Organization in the Industrial Communication Networks |
| title_fullStr | Hybrid Feed Forward Neural Networks and Particle Swarm Optimization for Intelligent Self-Organization in the Industrial Communication Networks |
| title_full_unstemmed | Hybrid Feed Forward Neural Networks and Particle Swarm Optimization for Intelligent Self-Organization in the Industrial Communication Networks |
| title_short | Hybrid Feed Forward Neural Networks and Particle Swarm Optimization for Intelligent Self-Organization in the Industrial Communication Networks |
| title_sort | hybrid feed forward neural networks and particle swarm optimization for intelligent self organization in the industrial communication networks |
| topic | Self-organization particle swarm optimization feed forward neural networks activation function bandwidth fitness evaluation |
| url | https://ieeexplore.ieee.org/document/10960282/ |
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