激励机制赋能高效雾网络

doi:10.11959/j.issn.1000-0801.2019061

电信科学 ›› 2019, Vol. 35 ›› Issue (3): 25-35.doi: 10.11959/j.issn.1000-0801.2019061

激励机制赋能高效雾网络

王蓓蓓¹,沈斐^2,³,李旭杰¹,徐慧慧^2,⁴,张冲冲^2,³

¹ 河海大学计算机与信息学院，江苏南京 211100
² 中国科学院上海微系统与信息技术研究所，上海 200050
³ 上海无线通信研究中心，上海 201210
⁴ 武汉大学，湖北武汉 430072

修回日期:2019-03-12 出版日期:2019-03-01 发布日期:2019-03-23
作者简介:王蓓蓓（1995- ），女，河海大学计算机与信息学院硕士生，主要研究方向为雾计算、移动边缘计算。|沈斐（1983- ），女，中国科学院上海微系统与信息技术研究所“百人计划”副研究员，主要研究方向为无线通信、雾计算、移动边缘计算。|李旭杰（1979- ），男，博士，河海大学计算机与信息学院副教授，主要研究方向为无线通信网络的资源分配与优化。|徐慧慧（1997- ），女，武汉大学在读，主要研究方向为雾计算、深度学习。|张冲冲（1994- ），男，中国科学院上海微系统与信息技术研究所硕士生，主要研究方向为雾计算、移动边缘计算。
基金资助:
国家自然科学基金资助项目(61871370);上海市自然科学基金资助项目(18ZR1437500);中国科学院百人计划项目(Y86BRA1001)

Incentive mechanism enables efficient fog network

Beibei WANG¹,Fei SHEN^2,³,Xujie LI¹,Huihui XU^2,⁴,Chongchong ZHANG^2,³

¹ College of Computer and Information,Hohai University,Nanjing 211100,China
² Shanghai Institute of Microsystem and Information Technology,Chinese Academy of Sciences,Shanghai 200050,China
³ Shanghai Research Center for Wireless Communications,Shanghai 201210,China
⁴ Wuhan University,Wuhan 430072,China

Revised:2019-03-12 Online:2019-03-01 Published:2019-03-23
Supported by:
The National Natural Science Foundation of China(61871370);The Natural Science Foundation of Shanghai of China(18ZR1437500);The Hundred-Talent Program of Chinese Academy of Sciences(Y86BRA1001)

摘要/Abstract

摘要：

为了提高雾网络的资源利用率以及服务质量，确保异构雾节点的可靠性与安全性，需要合理的激励机制。激励机制的引入，能够有效调动雾网络设备的服务积极性，减少设备恶意或自私等行为。首先提出了雾计算的研究意义，并给出了雾网络的特点及应用场景。其次介绍了雾网络的系统架构。然后将激励机制分别与雾网络中资源感知技术以及任务卸载技术相融合，并进一步给出了部分融合实例，体现了激励机制在雾网络中的重要作用。

关键词: 雾网络, 资源感知, 任务卸载, 激励机制

Abstract:

In order to improve the resource utilization and service quality of the fog network,as well as ensure the reliability and security of heterogeneous fog nodes,a reasonable incentive mechanism was required.The introduction of incentive mechanism can effectively motivate the service enthusiasm of fog network equipment and reduce the malicious or selfish behavior of the equipment.Firstly,the research significance of the fog computing was proposed,and the characteristics as well as the application scenarios of fog networks were given.Secondly,the system architecture of the fog network was introduced.Then the incentive mechanism was integrated with the resource sensing technology and the task offloading technology in the fog network,and further examples were presented to show the important role of the incentive mechanism in the fog network.

Key words: fog network, resource sensing, task offloading, incentive mechanism

中图分类号:

TP393

王蓓蓓,沈斐,李旭杰,徐慧慧,张冲冲. 激励机制赋能高效雾网络[J]. 电信科学, 2019, 35(3): 25-35.

Beibei WANG,Fei SHEN,Xujie LI,Huihui XU,Chongchong ZHANG. Incentive mechanism enables efficient fog network[J]. Telecommunications Science, 2019, 35(3): 25-35.

图/表 4

图1

图2

表1

图3

参考文献 28

[1]	Cisco.Cisco visual networking index:global mobile data traffic forecast update,2016-2021 white paper[R]. 2017.
[2]	方巍 . 从云计算到雾计算的范式转变[J]. 南京信息工程大学学报(自然科学版), 2016,8(5): 404-414.
	FANG W . Paradigm shift from cloud computing to fog computing[J]. Journal of Nanjing University of Information Science ＆Technology(Natural Science Edition), 2016,8(5): 404-414.
[3]	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.
[4]	MAO Y , YOU C , ZHANG J ,et al. A survey on mobile edge computing:the communication perspective[J]. IEEE Communications Surveys ＆ Tutorials, 2017,19(4): 2322-2358.
[5]	LIU Y , FIELDSEND J E , MIN G . A framework of fog computing:architecture,challenges,and optimization[J]. IEEE Access, 2017(5): 25445-25454.
[6]	WANG F , XU J , WANG X ,et al. Joint offloading and computing optimization in wireless powered mobile-edge computing systems[J]. IEEE Transactions on Wireless Communications, 2018,17(3): 1784-1797.
[7]	彭木根 . 5G 无线接入网络:雾计算和云计算[M]. 北京: 人民邮电出版社, 2018.
	PENG M G . 5G wireless access network:fog computing and cloud computing[M]. Beijing: Posts ＆ Telecom PressPress, 2018.
[8]	郭奕星 . 雾计算技术及其物联网应用[J]. 电信科学, 2018,34(Z1): 90-97.
	GUO Y X . Fog computing technology and its application in the Internet of things[J]. Telecommunications Science, 2018,34(Z1): 90-97.
[9]	ROMAN R , LOPEZ J , MAMBO M . Mobile edge computing,fog:a survey and analysis of security threats and challenges[J]. Future Generation Computer Systems, 2018(78): 680-698.
[10]	DAHIYA V , DALAL S . Fog computing:are view on integration of cloud computing and internet of things[C]// 2018 IEEE International Students’ Conference on Electrical Electronics and Computer Science,Oct 2-4,2018,Bangka Belitung,Indonesia. Piscataway:IEEE Press, 2018: 1-6.
[11]	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.
[12]	MUKHERJEE M , SHU L , WANG D . Survey of fog computing:fundamental,network applications,and research challenges[J]. IEEE Communications Surveys ＆ Tutorials, 2018,20(3): 1826-1857.
[13]	ALI M , RIAZ N , ASHRAF M I ,et al. Joint cloudlet selection and latency minimization in fog networks[J]. IEEE Transactions on Industrial Informatics, 2018,14(9): 4055-4063.
[14]	DU J , ZHAO L , FENG J ,et al. Computation offloading and resource allocation in mixed fog/cloud computing systems with min-max fairness guarantee[J]. IEEE Transactions on Communications, 2018,66(4): 1594-1608.
[15]	NING Z , HUANG J , WANG X . Vehicular fog computing:enabling real-time traffic management for smart cities[J]. IEEE Wireless Communications, 2019,26(1): 87-93.
[16]	SHAH-MANSOURI H , WONG V W S . Hierarchical fog-cloud computing for IoT systems:a computation offloading game[J]. IEEE Internet of Things Journal, 2018,5(4): 3246-3257.
[17]	CHEN M , HAO Y . Task offloading for mobile edge computing in software defined ultra-dense network[J]. IEEE Journal on Selected Areas in Communications, 2018,36(3): 587-597.
[18]	ZHANG C , SHEN F , ZHANG G ,et al. An incentive framework for collaborative sensing in fog networks[C]// 2018 10th International Conference on Wireless Communications and Signal Processing,Oct 18-20,2018,Hangzhou,China. Piscataway:IEEE Press, 2018: 1-6.
[19]	LIU Y , YU F R , LI X ,et al. Distributed resource allocation and computation offloading in fog and cloud networks with non-orthogonal multiple access[J]. IEEE Transactions on Vehicular Technology, 2018,67(12): 12137-12151.
[20]	LIU Z , YANG X , YANG Y ,et al. Dats:dispersive stable task scheduling in heterogeneous fog networks[J]. IEEE Internet of Things Journal,2018 accepted.
[21]	YANG Y , WANG K , ZHANG G ,et al. Meets:maximal energy efficient task scheduling in homogeneous fog networks[J]. IEEE Internet of Things Journal, 2018,5(5): 4076-4087.
[22]	周海平, 沈士根, 黄龙军 ,等. 基于博弈论的无线传感器网络恶意程序传播模型[J]. 电信科学, 2018,34(11): 67-76.
	ZHOU H P , SHEN S G , HUANG L J ,et al. Wireless sensor network malicious program transmission model based on game theory[J]. Telecommunications Science, 2018,34(11): 67-76.
[23]	LUO S , SUN Y , JI Y ,et al. Stackelberg game based incentive mechanisms for multiple collaborative tasks in mobile crowdsourcing[J]. Mobile Networks and Applications, 2016,21(3): 506-522.
[24]	LIU Y , WANG S , HUANG J ,et al. A computation offloading algorithm based on game theory for vehicular edge networks[C]// 2018 IEEE International Conference on Communications,May 20-24,2018,Kansas,MO,USA. Piscataway:IEEE Press, 2018: 1-6.
[25]	庄陵, 尹耀虎 . 认知异构网络中基于不完全频谱感知的资源分配算法[J]. 计算机科学, 2018,45(5): 49-53,74.
	ZHUAG L , YIN Y H . Resource allocation algorithm based on incomplete spectrum perception in cognitive heterogeneous networks[J]. Computer Science, 2018,45(5): 49-53
[26]	ABUSITTA A , BELLAICHE M , DAGENAISA M . A trust-based game theoretical model for cooperative intrusion detection in multi-cloud environments[C]// 2018 21st Conference on Innovation in Clouds,Internet and Networks and Workshops,Feb 18-21,2018,Paris,France. Piscataway:IEEE Press, 2018: 1-8.
[27]	CHANDRASEKHARAN S , KANDEEPAN S , GOMEZLINE K M ,et al. Terminal cooperation using game theory for energy efficiency in radio access networks:a simulation based performance analysis[C]// 2018 7th International Conference on Computer and Communication Engineering,Sep 19-20,2018,IIUM Ganbak,Malaysia. Piscataway:IEEE Press, 2018: 1-6.
[28]	ZHANG G , SHEN F , CHEN N ,et al. Dots:delay-optimal task scheduling among voluntary nodes in fog networks[J]. IEEE Internet of Things Journal, 2019:1.

	服务请求节点	网络运营商	雾节点
服务请求节点	拍卖博弈（不同的服务请求节点视为不同的竞买人，出价高者可得到更优质的雾节点服务）	Stackelberg 博弈（网络运营商作为博弈的领导者，服务请求节点作为博弈的跟随者，各自优化自身净收益）	合作博弈（服务请求节点与雾节点采取妥协方式，使二者总收益最大，依据二者在博弈中贡献或事先商定的契约分配收益）
网络运营商	合作博弈（服务请求节点与网络运营商采取妥协方式，使二者总收益最大，依据二者在博弈中贡献或事先商定的契约分配收益）	非合作博弈（在博弈中不同的网络运营商之间的利益相互影响，不同的运营商各自做出决策，使自身收益最大）	无限重复博弈（博弈重复无数次，若雾节点存在自私或恶意行为，将引发网络运营商的防御触发机制）
雾节点	有限重复博弈（博弈重复有限次，服务请求节点和雾节点根据之前的博弈历史制定下一步的行动策略）	Stackelberg 博弈（网络运营商作为博弈的领导者，雾节点作为博弈的跟随者，各自优化自身净收益）	贝叶斯博弈（参与博弈的各个雾节点对于对方的信息无法完全掌握，每个雾节点按各自的服务诚信度分为不同的类型，雾节点类型决定自身收益）

激励机制赋能高效雾网络

Incentive mechanism enables efficient fog network

在线阅读

PDF下载

可视化

摘要/Abstract

引用本文

使用本文

图/表 4

参考文献 28

相关文章 4

Metrics

推荐阅读 0

[1]	张天魁, 徐瑜, 刘元玮, 杨鼎成, 任元红. 无人机辅助MEC系统：架构、关键技术与未来挑战[J]. 电信科学, 2022, 38(8): 3-16.
[2]	绳韵, 许晨, 郑光远. 基于NOMA的超密集MEC网络任务卸载和资源分配方案[J]. 电信科学, 2022, 38(2): 35-46.
[3]	吴柳青,朱晓荣. 基于边-端协同的任务卸载资源分配联合优化算法[J]. 电信科学, 2020, 36(3): 42-52.
[4]	杨虎,张东戈,刘浩,白天松. 一种基于间接互惠的计算网格合作激励机制研究[J]. 电信科学, 2011, 27(9): 42-47.