区块链

智能生态网络:知识驱动的未来价值互联网基础设施

展开
  • 1. 北京大学 信息工程学院 深圳市内容中心网络与区块链重点实验室, 深圳 518055;
    2. 北京大学 互联网研究院, 深圳 518055
雷凯,副研究员,研究方向为命名数据网络、区块链、联邦学习.E-mail:leik@pkusz.edu.cn

收稿日期: 2019-10-31

  网络出版日期: 2020-01-19

基金资助

国家重大科技基础设施基金(发改高技[2016]2533号);深圳市内容中心网络与区块链重点实验室基金(No.ZDSYS201802051831427)资助

Intelligent Eco Networking (IEN): Knowledge-Driven and Value-Oriented Future Internet Infrastructure

Expand
  • 1. Shenzhen Key Lab for Information Centric Networking & Blockchain Technology, School of Electronics and Computer Engineering, Peking University, Shenzhen 518055, China;
    2. Internet Research Institute, Peking University, Shenzhen 518055, China

Received date: 2019-10-31

  Online published: 2020-01-19

摘要

未来互联网内容知识化、知识价值化、价值网络化、网络生态化、生态智能化的发展趋势已经越来越显著.针对传统IP互联网架构僵化、内容感知能力弱、多构架/多网络融合能力差、控制调度灵活性低、内生安全与信任维护机制缺失、服务质量模式单一、评价指标及方法落后等不足,创新性地提出智能生态网络(intelligent eco networking,IEN).IEN基于虚拟化、可编程设备、软硬结合的技术路线,改进信息中心网络构架构,综合分布式人工智能分析决策与区块链共识计算技术,考量存储、计算与带宽网络资源成本/效益指标,构建层次化、智能化、语义化的新型智联网络构架.IEN向后兼容IP协议,向前演进面向跨域、边缘重点场景的命名(或标识)与IP融合异质计算寻址的多模态网络协议,叠加内容、身份鉴授权与多方可信激励机制,增强网络资源分配模型和优化评价体系.通过内容语义检测与身份可信鉴授权,IEN能坚持安全可控与开放包容并重,旨在形成一个高扩展、动态适应、多目标优化的网络基础设施,砥砺探索新一代产业化、经济化、生态化未来互联网,奠定一个开放与共享、协同互惠的智能生态网络.

本文引用格式

雷凯, 黄硕康, 方俊杰, 黄济乐, 谢英英, 彭波 . 智能生态网络:知识驱动的未来价值互联网基础设施[J]. 应用科学学报, 2020 , 38(1) : 152 -172 . DOI: 10.3969/j.issn.0255-8297.2020.01.012

Abstract

As the trend of knowledgelization of content, evaluation of knowledge, networking of value, ecologicalization of network and intellectualization of ecology is becoming increasingly prominent in the future Internet. In this paper, we propose the con-cept of intelligent eco networking (IEN) for the future Internet, countering with the deficiencies of the current IP networks, including rigid architecture, weak content awareness, poor multi-architecture/multi-network integration, low scheduling flexibility, lacking endogenous security and trust maintenance mechanism, single quality of service (QoS) mode and outdated evaluation indicators and methods. IEN adopts software and hardware integrated technology roadmap based on virtualization and configurable devices, improves information-centric networking (ICN) architecture, integrates distributed artificial intelligence (AI) analysis decision and blockchain consensus computing technology, considers network resource cost/profit indicators regarding storage, computing and bandwidth resources, aims at building a hierarchical, intelligent and semantic network architecture. IEN is backward compatible with IP protocol; forward evolves towards naming and IP integrated, heterogeneous computing addressing and multi-modal network protocol for cross-domain, edge-critical scenario, overlays content, identity authentication and multi-party trusted incentive mechanism; optimizes network resources allocation model and evaluation system. Through content semantic detection and identity credibility authentication, IEN insists on both security and openness. IEN aims to form a network infrastructure with high expansion, dynamic adaptation, and multi-objective optimization. It explores a new generation of industrialization, economics, and ecological Internet, and establishes an intelligent network of openness, sharing and synergy.

参考文献

[1] Peterson L L, Davie B S. Computer networks:a systems approach[M].[S.l.]:Elsevier, 2007.
[2] Atzori L, Iera A, Morabito G. The Internet of Things:a survey[J]. Computer Networks, 2010, 54(15):2787-2805.
[3] Shi W, Cao J, Zhang Q, et al. Edge computing:vision and challenges[J]. IEEE Internet of Things Journal, 2016, 3(5):637-646.
[4] Nilsson N J. Principles of artificial intelligence[M]. San Francisco:Morgan Kaufmann, 2014.
[5] Russell S J, Norvig P. Artificial intelligence:a modern approach[M]. Malaysia:Pearson Education Limited, 2016.
[6] Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[C]//Advances in Neural Information Processing Systems, 2012:1097-1105.
[7] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016:770-778.
[8] Hinton G, Deng L, Yu D, et al. Deep neural networks for acoustic modeling in speech recognition[J]. IEEE Signal Processing Magazine, 2012:29.
[9] Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate[OL]. 2014. arXiv Preprint arXiv:1409.0473.
[10] Kim H, Gupta A. Ontas:flexible and scalable online network traffic anonymization system[C]//Proceedings of the 2019 Workshop on Network Meets AI&ML. ACM, 2019:15-21.
[11] Su K, Li J, Fu H. Smart city and the applications[C]//2011 International Conference on Electronics, Communications and Control (ICECC). IEEE, 2011:1028-1031.
[12] Chaib-Draa B. Readings in agents[M]. San Francisco:Morgan Kaufmann, 1998.
[13] Smith V, Chiang C K, Sanjabi M, et al. Federated multi-task learning[C]//Advances in Neural Information Processing Systems, 2017:4424-4434.
[14] Turing S. Intelligent eco networking (IEN):an advanced future Internet of intelligence for digital social economic ecosystem[C]//20181st IEEE International Conference on Hot InformationCentric Networking (HotICN). IEEE, 2018:179-185.
[15] Shang W, Yu Y D, Droms R, et al. Challenges in IoT networking via tcp/ip architecture[R]. Technical Report NDN-0038. NDN Project, 2016.
[16] Rankin P J, Griffiths J C. Distributed location based service system:US 6879838[P]. 2005-12.
[17] Mohurle S, Patil M. A brief study of Wannacry threat:Ransomware attack[J]. International Journal of Advanced Research in Computer Science, 2017, 8(5).
[18] Zhang L, Afanasyev A, Burke J, et al. Named data networking[J]. ACM SIGCOMM Computer Communication Review, 2014, 44(3):66-73.
[19] Ahlgren B, Dannewitz C, Imbrenda C. et al. A survey of information-centric networking[J]. IEEE Communications Magazine, 2012, 50(7):26-36.
[20] Shang W, Bannis A, Liang T, et al. Named data networking of things[C]//2016 IEEE First International Conference on Internet-of-Things Design and Implementation (IoTDI). IEEE, 2016:117-128.
[21] Raychaudhuri D, Nagaraja K, Venkataramani A. Mobilityfirst:a robust and trustworthy mobility-centric architecture for the future internet[J]. ACM SIGMOBILE Mobile Computing and Communications Review, 2012, 16(3):2-13.
[22] Anderson T, Birman K, Broberg R, et al. The nebula future internet architecture[M]//The Future Internet Assembly.[S.l.]:Springer, 2013:16-26.
[23] Anand A, Dogar F, Han D, et al. Xia:an architecture for an evolvable and trustworthy Internet[C]//Proceedings of 10th ACM Workshop on Hot Topics in Networks. ACM, 2011:2.
[24] Wolf T, Griffioen J, Calvert K L, et al. Choicenet:toward an economy plane for the internet[J]. ACM SIGCOMM Computer Communication Review, 2014, 44(3):58-65,
[25] Yuan H, Song T, Crowley P. Scalable NDN forwarding:concepts, issues and principles[C]//201221st International Conference on Computer Communications and Networks (ICCCN). IEEE, 2012:1-9.
[26] Lei K, Qin M, Bai B, et al. GCN-GAN:a non-linear temporal link prediction model for weighted dynamic networks[C]//IEEE INFOCOM 2019-IEEE Conference on Computer Communications. IEEE, 2019:388-396.
[27] Jin T, Zhang X, Liu Y, et al. Block NDN:a bitcoin blockchain decentralized system over named data networking[C]//2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN). IEEE, 2017:75-80.
[28] Zhou Z, Li L, Li Z. Efficient cross-domain authentication scheme based on blockchain technology[J]. Journal of Computer Applications, 2018, 38(2):316-320.
[29] Nunes B A A, Mendonca M, Nguyen X N, et al. A survey of software-defined networking:past, present, and future of programmable networks[J]. IEEE Communications Surveys&Tutorials, 2014, 16(3):1617-1634.
[30] 韩庆绵.人机物互联的智能服务系统架构研究[J].无线电工程,2017, 47(11):17-21. Han Q M. Research on architecture of intelligent service system about man-machine-object interactive network[J]. Radio Engineering, 2017, 47(11):17-21.(in Chinese)
[31] Kreutz D, Ramos F M V, Verissimo P E. et al. Software-defined networking:a comprehensive survey[J]. Proceedings of the IEEE, 2014, 103(1):10-13.
[32] Rothenberg C E, Nascimento M R, Salvador M R, et al. Revisiting routing control platforms with the eyes and muscles of software-defined networking[C]//ACM SIGCOMM Workshop on Hot Topics in Software Defined Networking, 2012.
[33] Li X, Jiang P, Chen T, et al. A survey on the security of blockchain systems[J]. Future Generation Computer Systems, 2017. DOI:10.1016/j.future.2017.08.020
[34] Jacobson V, Smetters D K, Thornton J D, et al. Networking named content[C]//Proceedings of the 5th International Conference on Emerging Networking Experiments and Technologies. ACM, 2009:1-12.
[35] Josep A D, Katz R, Konwinski A, et al. A view of cloud computing[J]. Communications of the ACM, 2010, 53(4).
[36] Bonomi F, Milito R, Zhu J, et al. Fog computing and its role in the Internet of Things[C]//Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing. ACM, 2012:13-16.
[37] 郭爱鹏,周光涛,夏俊杰,等.基于SDN的边缘网络控制技术及应用[J].邮电设计技术,2014(3):35-39. Guo A P, Zhou G T, Xia J J, et al. Edge network control technology based on SDN and its application[J]. Designing Techniques of Posts and Telecommunications,2014(3):35-39.(in Chinese)
[38] 郑植庆.高校校园网出口方案设计与研究[J].信息系统工程,2013, 12:42.
[39] Mattila J. The blockchain phenomenon-the disruptive potential of distributed consensus architectures[R]. ETLA Working Papers, 2016.
[40] Lei K, Zhang Q, Lou J, et al. Securing ICN-based UAV ad hoc networks with blockchain[J]. IEEE Communications Magazine, 2019, 57(6):26-32.
[41] Wu X, Kumar V, Quinlan J R, et al. Top 10 algorithms in data mining[J]. Knowledge and Information Systems, 2008, 14(1):1-37.
[42] Mcmahan H B, Ramage D, Talwar K, et al. Learning differentially private recurrent language models[J/OL]. 2017. arXiv preprint arXiv:1710.06963
[43] Yang Q, Liu Y, Chen T, Tong Y. Federated machine learning:concept and applications[J]. ACM Transactions on Intelligent Systems and Technology, 2019, 10(2):1-19.
[44] Xie P, Bilenko M, Finley T, et al. Crypto-nets:neural networks over encrypted data[J/OL]. 2014.arXiv preprint arXiv:1412.6181.
文章导航

/