摘要
With the advancements of the next-generation communication networking and Internet ofThings(IoT)technologies,a variety of computation-intensive applications(e.g.,autonomous driving and face recognition)have emerged.The execution of these IoT applications demands a lot of computing resources.Nevertheless,terminal devices(TDs)usually do not have sufficient computing resources to process these applications.Offloading IoT applications to be processed by mobile edge computing(MEC)servers with more computing resources provides a promising way to address this issue.While a significant number of works have studied task offloading,only a few of them have considered the security issue.This study investigates the problem of spectrum allocation and security-sensitive task offloading in an MEC system.Dynamic voltage scaling(DVS)technology is applied by TDs to reduce energy consumption and computing time.To guarantee data security during task offloading,we use AES cryptographic technique.The studied problem is formulated as an optimization problem and solved by our proposed efficient offloading scheme.The simulation results show that the proposed scheme can reduce system cost while guaranteeing data security.
基金
supported in part by Key Scientific Research Projects of Colleges and Universities in Anhui Province(2022AH051921)
Science Research Project of Bengbu University(2024YYX47pj,2024YYX48pj)
Anhui Province Excellent Research and Innovation Team in Intelligent Manufacturing and Information Technology(2023AH052938)
Big Data and Machine Learning Research Team(BBXYKYTDxj05)
Funding Project for the Cultivation of Outstanding Talents in Colleges and Universities(gxyqZD2021135)
the Key Scientific Research Projects of Anhui Provincial Department of Education(2022AH051376)
Start Up Funds for Scientific Research of High-Level Talents of Bengbu University(BBXY2020KYQD02)
Scientific Research and Development Fund of Suzhou University(2021fzjj29)
Research on Grain Logistics Data Processing and Safety Issues(ALAQ202401017)
the Open Fund of State Key Laboratory of Tea Plant Biology and Utilization(SKLTOF20220131)
funded by the Ongoing Research Funding Program(ORF-2025-102),King Saud University,Riyadh,Saudi Arabia.
