Jul 2026· Journal of universal computer science (Online)· Vol 32, pp. 1071-1100· 0 citations· 16 references
Computer Science
TL;DR
An Energy Optimized Network Route Cluster Bandwidth (EONRCB) is an Enhanced Service Data Transmission (ESDT) model proposed in this paper to circumvent problems of data leakage and packet misinsertion and achieves higher energy efficiency, reduced congestion, and a longer network lifespan.
Abstract
The development of sixth-generation (6G) mobile communications aims to deliver super-fast connections at terahertz frequencies, surpassing those of 5G. Nonetheless, these improvements come with challenges of data leakage and packet misinsertion, and they place greater pressure on secure and efficient routing. An Energy Optimized Network Route Cluster Bandwidth (EONRCB) is an Enhanced Service Data Transmission (ESDT) model proposed in this paper to circumvent these problems. The model contains several significant factors, such as Transmission Node Support Weight (TNSW), which evaluates network traffic and petite access control, which provides packet fragmentation and a secure route. Then, the Service Level Route Count Rollback Node Aggregator (SLR-CRNA), which validates active route node interaction. Further, the Sleeper Node Controller improves the consistency of node operations, whereas Slicing Neighbour Node Path Routing (SN2PR) introduces consistency in the source-to-destination connectivity within edge networks. Additional optimisation can be achieved through functionality such as Priority Cycle Tags (PCT) and Recursive Scheduling Time (RST), which provide mechanisms to improve overall RTPS throughput and node scheduling by minimising latency via a depth-first task alignment scheme. The suggested architecture achieves higher energy efficiency, reduced congestion, and a longer network lifespan. The performance of the proposed 6G framework will be validated against existing models to assess its effectiveness.
This paper conducts a comprehensive literature survey to examine existing optimization techniques and proposes an improved methodology leveraging AI-driven resource allocation and dynamic spectrum sharing that demonstrates the effectiveness of the proposed approach in reducing end-to-end latency and improving network reliability.
Kenji Sato· International Journal of Mod...· 0 citations
This paper conducts a comprehensive literature survey to examine existing optimization techniques and proposes an improved methodology leveraging AI-driven resource allocation and dynamic spectrum sharing that demonstrates the effectiveness of the proposed approach in reducing end-to-end latency and improving network reliability.
William Hughes, Marta Silva· International Journal of Dat...· 0 citations
A novel energy-efficient multipath routing for load balancing (EMRD-LB) is capable of improving the routing performance and reducing delay, reduces packets dropping and improves data receiving.
Aradhana Saxena, Nitika Vats· International journal of com...· 0 citations
Deep Q-learning with a rapidly converging local search method based on permutation-equivariant neural networks for unseen environments in the given network scenarios is incorporated to ensure faster convergence and a minimal memory footprint.
M. Lakshmi, Arram. Mahesh Babu· Journal of Circuits, Systems...· 0 citations
It is concluded that AI is not merely an enabler but a cornerstone for realizing sustainable and intelligent 6G networks, paving the way for an eco-friendly digital future.
Joshna M, R. K.· Journal of Artificial Intell...· 0 citations
A novel cluster-based routing protocol that integrates a Fungal Growth Optimizer for adaptive cluster head (CH) selection and a Graph Neural Network for inter-cluster routing, which demonstrates FGOGNN’s potential for deployment in real-time WSN applications, where energy efficiency and dynamic adaptability are paramount.
Huang-shui Hu, Shuo Liu, Qier Kang et al.· Symmetry· 1 citation
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