View in EDS

6G Deep Federated Optimization for Intelligent Data Quantum‐Resistant Routing and Monitoring System Using Game Theory.

Saved in:
Bibliographic Details
Title: 6G Deep Federated Optimization for Intelligent Data Quantum‐Resistant Routing and Monitoring System Using Game Theory.
Authors: Mohanasundaram, S., Amudha, K., Priya, V., Karpagam, N. Shunmuga
Source: International Journal of Communication Systems; 1/10/2026, Vol. 39 Issue 1, p1-23, 23p
Subject Terms: WIRELESS sensor networks, FEDERATED learning, GAME theory, COMPUTER network monitoring, BROADBAND communication systems, DATA transmission system security measures
Abstract: The efficient path selection of data in wireless sensor networks is vital for enhancing total system performance. Traditional path selection protocols encounter challenges related to frequent node movements, optimization of energy efficiency, and the nature of network environments. To tackle these challenges, an innovative methodology termed Deep reinforcement arctic puffin federated stochastic gradient learning (DRAFSGL) integrates federated learning and puffin optimization for energy‐efficient multipath routing. Multiview deep embedded clustering (MDEC), enhanced by coyote and badger optimization into efficient clusters to reduce routing complexity. To ensure privacy, quantum‐resistant homomorphic encryption (QRHE) enables secure computation without safeguarding against quantum threats. The data monitoring system applies game theory to optimize the behavior of agents involved in monitoring activities in the system. The suggested approach has exhibited extraordinary evaluation, attaining a peak accuracy of 99.7% alongside a precision of 98.9%, a specificity of 98.4%, a delay of 18 ms, a throughput of 87.9 Kbps, and a remarkable recall rate of 98.9%, as well as an F1‐score when compared to existing methods. Overall, DRAFSGL methodology improves adaptive, secure, and energy‐efficient multipath routing through the combination of federated learning and arctic puffin optimization. Meanwhile, the MDEC technique with improved coyote and badger optimization (ICBO) simplifies routing complexity and QRHE guarantees quantum‐resistant, secure data transmission, effectively addressing the shortcomings of current techniques in dynamic 6G wireless sensor network (WSN)‐based Internet of Things (IoT) systems. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Communication Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Complementary Index
Description
Abstract:The efficient path selection of data in wireless sensor networks is vital for enhancing total system performance. Traditional path selection protocols encounter challenges related to frequent node movements, optimization of energy efficiency, and the nature of network environments. To tackle these challenges, an innovative methodology termed Deep reinforcement arctic puffin federated stochastic gradient learning (DRAFSGL) integrates federated learning and puffin optimization for energy‐efficient multipath routing. Multiview deep embedded clustering (MDEC), enhanced by coyote and badger optimization into efficient clusters to reduce routing complexity. To ensure privacy, quantum‐resistant homomorphic encryption (QRHE) enables secure computation without safeguarding against quantum threats. The data monitoring system applies game theory to optimize the behavior of agents involved in monitoring activities in the system. The suggested approach has exhibited extraordinary evaluation, attaining a peak accuracy of 99.7% alongside a precision of 98.9%, a specificity of 98.4%, a delay of 18 ms, a throughput of 87.9 Kbps, and a remarkable recall rate of 98.9%, as well as an F1‐score when compared to existing methods. Overall, DRAFSGL methodology improves adaptive, secure, and energy‐efficient multipath routing through the combination of federated learning and arctic puffin optimization. Meanwhile, the MDEC technique with improved coyote and badger optimization (ICBO) simplifies routing complexity and QRHE guarantees quantum‐resistant, secure data transmission, effectively addressing the shortcomings of current techniques in dynamic 6G wireless sensor network (WSN)‐based Internet of Things (IoT) systems. [ABSTRACT FROM AUTHOR]
ISSN:10745351
DOI:10.1002/dac.70326