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Authors
Wang, Zhe
Zhou, Lijuan
He, Jiekai
Ge, Lina
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Funding
Funding for this research was provided by:
Natural Science Foundation of Guangxi Zhuang Autonomous Region (2024GXNSFAA010111, 2020GXNSFBA297103, 2024GXNSFAA010111, 2020GXNSFBA297103)
Guangxi University for Nationalities' "Xiangsi Lake Youth Scholar Innovation Team" Project (2023GXUNXSHQN02)
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Article History
Received: 7 January 2025
Revised: 24 July 2025
Accepted: 3 August 2025
First Online: 17 October 2025
Declarations
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: The authors declare no competing interests.
: The research presented in this manuscript titled"Task Offloading Optimization in IRS-Assisted Multi-Tier Computing Networks"complies with all relevant ethical guidelines and regulations.The submitted work is original. A single study has not been split up into several parts to increase the quantity of submissions(i.e.,'salami-slicing/publishing').Results are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation (including image-based manipulation). We have adhered to discipline-specific rules for acquiring, selecting, and processing data.No data, text, or theories by others are presented as if they were our own ('plagiarism'). Quotation marks are used for verbatim copying of material.The authors confirm that all ethical responsibilities have been fulfilled in conducting this research and preparing this manuscript.al, and permissions secured for material that is copyrighted.
