面向5G雾计算中基于Q-learning的安全中继节点选择方法

涂山山; 于金亮; 孟远; WWAQAS M; 刘雷

doi:10.11959/j.issn.1000-0801.2019176

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面向5G雾计算中基于Q-learning的安全中继节点选择方法

专题：5G | 更新时间：2024-06-05

- 面向5G雾计算中基于Q-learning的安全中继节点选择方法
- Secure relay node selection method based on Q-learning for fog computing in 5G network
- 电信科学 2019年35卷第7期页码：60-68
- 作者机构：
  
  1. 北京工业大学信息学部，北京100124
  2. 可信计算北京市重点实验室，北京100124
  3. 清华大学电子工程系，北京 100084
  4. 北京机电工程研究所，北京 100074
- 作者简介：
  
  [ "涂山山（1983- ），男，北京工业大学信息学部、可信计算北京市重点实验室副教授、硕士生导师，主要研究方向为云计算、MEC、信息安全技术。" ]
  [ "于金亮（1996- ），男，北京工业大学信息学部、可信计算北京市重点实验室硕士生，主要研究方向为雾计算、机器学习、信息安全技术。" ]
  [ "孟远（1995- ），男，北京工业大学信息学部、可信计算北京市重点实验室硕士生，主要研究方向为雾计算、强化学习、信息安全技术。" ]
  [ "WAQAS M（1985- ），男，清华大学电子工程系博士生，主要研究方向为 5G 网络、MEC、信息安全技术。" ]
  [ "刘雷（1982- ），男，北京机电工程研究所高级工程师，主要研究方向为航空电子、机电、机器人和智能系统。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61801008);国家重点研发计划经费资助项目;The National Key Research and Development Program of China(2018YFB0803600);北京市自然科学基金资助项目;Beijing Natural Science Foundation (No. L172049),Beijing Municipal Commission of Science and Technology(L172049);北京市教委科研计划经费资助项目;The Scientic Research Common Program of Beijing Municipal Commission of Education(KM201910005025)
- DOI：10.11959/j.issn.1000-0801.2019176
  中图分类号： TP393
- 网络出版日期：2019-07，
  
  纸质出版日期：2019-07-20
- 稿件说明：
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涂山山, 于金亮, 孟远, 等. 面向5G雾计算中基于Q-learning的安全中继节点选择方法[J]. 电信科学, 2019,35(7):60-68.

Shanshan TU, Jinliang YU, Yuan MENG, et al. Secure relay node selection method based on Q-learning for fog computing in 5G network[J]. Telecommunications science, 2019, 35(7): 60-68.
涂山山, 于金亮, 孟远, 等. 面向5G雾计算中基于Q-learning的安全中继节点选择方法[J]. 电信科学, 2019,35(7):60-68. DOI： 10.11959/j.issn.1000-0801.2019176.

Shanshan TU, Jinliang YU, Yuan MENG, et al. Secure relay node selection method based on Q-learning for fog computing in 5G network[J]. Telecommunications science, 2019, 35(7): 60-68. DOI： 10.11959/j.issn.1000-0801.2019176.

摘要

提出了一种基于 Q-learning 的最优双中继节点选择方法。首先构建了基于社会意识的安全雾计算结构模型，然后在该模型下设计了基于 Q-learning 算法的最优双中继节点选择方法，实现了在动态环境下对最优双中继节点的选择，最后对密钥生成速率、双中继节点选择速度和动态环境中双中继节点的选择准确率进行了分析。实验结果表明，该方案能有效地在动态环境中选择最优双中继节点，算法迅速收敛达到稳定，最优中继节点选择速度得到有效提升。

Abstract

A Q-learning-based optimal dual-relay node selection method was proposed. Firstly

a security fog computing structure model based on social awareness was constructed

and then an optimal dual-relay node selection method based on Q-learning algorithm was designed under this model

which achieved the selection of optimal dual-relay nodes in dynamic environment. Finally

the key generation rate

the selection speed of dual-relay nodes and the selection accuracy of dual-relay nodes in dynamic environment were analyzed. The experimental results show that the scheme can effectively select the optimal dual-relay nodes in dynamic environment

the algorithm converges rapidly to a stable level

and the selection speed of the optimal relay node is effectively improved.

关键词

Keywords

references

Cisco . Cisco delivers vision of fog computing to accelerate value from billions of connected devices [R ] . 2017 .

SARKAR S , CHATTERJEE S , MISRA S . Assessment of the suitability of fog computing in the context of internet of things [J ] . IEEE Transactions on Cloud Computing , 2018 , 6 ( 1 ): 46 - 59 .

ALRAWAIS A , ALHOTHAILY A , HU C , et al . Fog computing for the internet of things: security and privacy issues [J ] . IEEE Internet Computing , 2017 , 21 ( 2 ): 34 - 42 .

CHAUDHARY R , KUMAR N , ZEADALLY S . Network service chaining in fog and cloud computing for the 5G environment: data management and security challenges [J ] . IEEE Communications Magazine , 2017 , 55 ( 11 ): 114 - 122 .

王庆扬 , 谢沛荣 , 熊尚坤 . 5G 关键技术与标准综述 [J ] . 电信科学 , 2017 , 33 ( 11 ): 112 - 122 .

WANG Q Y , XIE P R , XIONG S K . Key technology and standardization progress for 5G [J ] . Telecommunications Science , 2017 , 33 ( 1 ): 112 - 122 .

朱雪田 , 夏旭 , 齐飞 . 5G 网络关键技术和业务 [J ] . 电子技术应用 , 2018 , 44 ( 9 ): 1 - 4 .

ZHU X T , XIA X , QI F . 5G key technologies and business forecast [J ] . Application of Electronic Technique , 2018 , 44 ( 9 ): 1 - 4 .

BOTTA A , DONATO W D , PERSICO V , et al . Integration of cloud computing and internet of things: a survey [J ] . Future Generation Computer Systems , 2016 , 56 ( 1 ): 684 - 700 .

SINGH S P , NAYYAR A , KUMAR R , et al . Fog computing: from architecture to edge computing and big data processing [J ] . The Journal of Supercomputing , 2019 , 75 ( 4 ): 2070 - 2105 .

MA Z , XIE J , LI H , et al . The role of data analysis in the development of intelligent energy networks [J ] . IEEE Network , 2017 , 31 ( 5 ): 88 - 95 .

SHANNON C E . Communication theory of secrecy systems [J ] . Bell System Technical Journal , 1949 , 28 ( 4 ): 656 - 715 .

WYNER A D . The wire-tap channel [J ] . Bell System Technical Journal , 1975 , 54 ( 8 ): 1355 - 1387 .

MAURER U M . Secret key agreement by public discussion from common information [J ] . IEEE Transactions on Information Theory , 1993 , 39 ( 3 ): 733 - 742 .

ZENG K . Physical layer key generation in wireless networks: challenges and opportunities [J ] . IEEE Communications Magazine , 2015 , 53 ( 6 ): 33 - 39 .

THAI C D T , LEE J , TONY Q S , et al . Physical-layer secret key generation with colluding untrusted relays [J ] . IEEE Transactions on Wireless Communications , 2016 , 15 ( 2 ): 1517 - 1530 .

胡晓言 , 金梁 , 黄开枝 , 等 . 基于信号传播特性的物理层密钥生成方案 [J ] . 电子学报 , 2019 , 47 ( 2 ): 483 - 488 .

HU X Y , JIN L , HUANG K Z , et al . Physical layer secret key generation scheme based on signal propagation characteristics [J ] . ACTA Electronica Sinica , 2019 , 47 ( 2 ): 483 - 488 .

TU S , WAQAS M , REHMAN S U , et al . Security in fog somputing: anovel technique to tackle an impersonation attack [J ] . IEEE Access , 2018 ( 6 ): 74993 - 75001 .

WAQAS M , AHMED M , LI Y , et al . Social-aware secret key generation for secure device-to-device communication via trusted and non-trusted relays [J ] . IEEE Transactions on Wireless Communications , 2018 , 17 ( 6 ): 3918 - 3930 .

AHLSWEDE R , CSISZÁR I . Common randomness in information theory and cryptography.Part I: secret sharing [J ] . IEEE Transactions on Information Theory , 1993 , 39 ( 4 ): 1121 - 1132 .

张启星 , 付敬奇 . 基于信道特征提取的物理层安全密钥生成方法 [J ] . 电子测量与仪器学报 , 2019 , 33 ( 1 ): 16 - 22 .

ZHANG Q X , FU J Q . Physical layer secure key generation method based on channel feature extraction [J ] . Journal of Electronic Measurement and Instrumentation , 2019 , 33 ( 1 ): 16 - 22 .

孟远 , 涂山山 , 于金亮 . 雾计算中基于DQL算法的伪装攻击检测方案 [J ] . 计算机工程与应用 , 2019 ( 已录用 ).

MENG Y , TU S S , YU J L . Detection scheme of camouflage attack based on DQL algorithms in fog computing [J ] . Computer Engineering and Applications , 2019 ( accepted ).

MNIH V , KAVUKCUOGLU K , SILVER D , et al . Human-level control through deep reinforcement learning [J ] . Nature , 2015 , 518 ( 7540 ): 529 - 533 .

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