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Corresponding Author

Archana Rajendra Mane

ORCID

Archana: 0009-0003-7707-7890 ;Sachin: 0009-0005-8856-8905

Subject Area

Electronics and Communication Engineering

Article Type

Original Study

Abstract

Researchers are currently investigating 6G wireless communication technologies because they can offer ultra-low latency, high data rates, and smarter network management than 5G. IoT services have spurred the development of cutting-edge technologies such as quantum communications, terahertz (THz), and artificial intelligence (AI), all of which are expected to be included into the subsequent generation of 6G networks. This paper presents a regression-based data collection algorithm for 6G-enabled IoT networks that dynamically prioritizes data through Random Forest regression. With a Mean Absolute Error (MAE) of 0.62 ms and a Root Mean Squared Error (RMSE) of 0.83 ms, the proposed method can accurately predict packet processing delay. The simulation results show that compared to traditional FIFO scheduling, the average latency is 35% lower, the packet delivery ratio is 25% higher under heavy traffic, and the cumulative processing delay is 13% lower. These results show that the suggested method greatly improves the performance and responsiveness of systems in large-scale IoT settings

Keywords

Artificial Intelligence; B5G/6G; Future Communication System; Future IoT; Network Optimization

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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