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Energy Efficient 6G performance Improvement based on Service Level Recursive Scheduling routing using Priority Cycle Tags

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.

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