群体智能2.0研究综述

doi:10.19678/j.issn.1000-3428.0056410

计算机工程 ›› 2019, Vol. 45 ›› Issue (12): 1-7. doi: 10.19678/j.issn.1000-3428.0056410

群体智能2.0研究综述

赵健, 张鑫褆, 李佳明, 贺晨

西北大学信息科学与技术学院, 西安 710127

收稿日期:2019-09-05 修回日期:2019-10-05 出版日期:2019-12-15 发布日期:2019-12-31
作者简介:赵健(1973-),男,教授、博士后、科技创新2030"新一代人工智能"重大项目群体智能方向会评专家,主研方向为人工智能及其应用;张鑫褆、李佳明,硕士研究生;贺晨,教授、博士。
基金资助:
国家自然科学基金（61701401）；陕西省自然科学基金（2019KJXX-061）。

Research Review of Crowd Intelligence 2.0

ZHAO Jian, ZHANG Xinti, LI Jiaming, HE Chen

School of Information Science and Technology, Northwest University, Xi'an 710127, China

Received:2019-09-05 Revised:2019-10-05 Online:2019-12-15 Published:2019-12-31

摘要/Abstract

摘要： 在物联网、移动互联网的快速发展和大数据的深层驱动下，群体智能进入2.0时代。通过介绍群体智能2.0的定义，分析国内科研团队在群智感知计算、联邦学习及众包等方面的研究成果，明确群体智能2.0的发展趋势，并指出群智感知计算与众包技术将成为群体智能2.0的主要研究方向，数据获取、数据处理及用户隐私保护为需要解决的关键技术难点。

关键词: 群体智能, 众包, 移动众包, 群智感知计算, 联邦学习

Abstract: With the rapid development of Internet of Things(IoT) as well as mobile Internet and the deep drive of big data,crowd intelligence has entered the 2.0 era.This paper introduces the definition ofcrowd intelligence 2.0,and analyzes the research results of Chinese research teams on crowd intelligence sensing and computing,federated learning and crowdsourcing.On this basis,this paper clearly indicates the development trend of crowd intelligence 2.0,where crowdsourcing technologies and crowd intelligence sensing and computing will become the focus of research,while data acquisition,data processing and user privacy protection will become the key technological challenges to be solved.

Key words: crowd intelligence, crowdsourcing, mobile crowdsourcing, crowd intelligence sensing and computing, federated learning

中图分类号:

TP18

赵健, 张鑫褆, 李佳明, 贺晨. 群体智能2.0研究综述[J]. 计算机工程, 2019, 45(12): 1-7.

ZHAO Jian, ZHANG Xinti, LI Jiaming, HE Chen. Research Review of Crowd Intelligence 2.0[J]. Computer Engineering, 2019, 45(12): 1-7.

http://www.ecice06.com/CN/Y2019/V45/I12/1

参考文献

[1] COLORNI A,DORIGO M,MANIEZZO V.Distributed optimization by ant colonies[C]//Proceedings of European Conference on Artificial Life.Paris,France:Elsevier Publishing,1991:134-142.
[2] CHU Shuchuan,RODDICK J F,SU C J.Constrained ant colony optimization for data clustering[C]//Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence.Berlin,Germany:Springer,2004:534-543.
[3] KENNEDY J,EBERHART R C.Particle swarm optimization[C]//Proceedings of IEEE International Conference on Neural Networks.Washington D.C.,USA:IEEE Press,1995:1942-1948.
[4] LI Wei,WU Wenjun,WANG Huaimin,et al.Crowd intelligence in AI 2.0 era[J].Frontiers of Information Technology and Electronic Engineering,2017,18(2):15-43.
[5] LICKLIDER J C R.Man-computer symbiosis[J].IRE Transactions on Human Factors in Electronics,1960,1(2):4-11.
[6] Development planning for a new generation of artificial intelligence[EB/OL].[2019-09-24].http://www.gov.cn/zhengce/content/2017-07/20/content_5211996.htm.(in Chinese)新一代人工智能发展规划[EB/OL].[2019-09-24].http://www.gov.cn/zhengce/content/2017-07/20/content_5211996.htm.
[7] WANG Mei,ZHU Yunlong,HE Xiaoxian.A survey of swarm intelligence[J].Computer Engineering,2005,31(22):204-206.(in Chinese)王玫,朱云龙,何小贤.群体智能研究综述[J].计算机工程,2005,31(22):204-206.
[8] LIU Yunhao.Crowd sensing and computing[J].Communications of the CCF,2012,8(10):38-41.(in Chinese)刘云浩.群智感知计算[J].中国计算机学会通讯,2012,8(10):38-41.
[9] GUO Wenzhong,ZHU Weiping,YU Zhiyong,et al.A survey of task allocation:contrastive perspectives from wireless sensor networks and mobile crowdsensing[J].IEEE Access,2019,7:78406-78420.
[10] GUO Bin,ZHAI Shuying,YU Zhiwen,et al.Crowdsensing big data:sensing,data selection,and understanding[J].Big Data Research,2017,3(5):57-69.(in Chinese)郭斌,翟书颖,於志文,等.群智大数据:感知、优选与理解[J].大数据,2017,3(5):57-69.
[11] ABUALSAUD K.A survey on mobile crowd-sensing and its applications in the IoT era[J].IEEE Access,2019,7:3855-3881.
[12] LUAN Dongming,YANG Yongjia,WANG En,et al.An efficient target tracking approach through mobile crowdsen-sing[J].IEEE Access,2019,7:110749-110760.
[13] WU Bin,ZHENG Yi,FU Weipeng,et al.A customer behavior analysis algorithm based on swarm intelligence[J].Chinese Journal of Computers,2003,26(8):913-918.(in Chinese)吴斌,郑毅,傅伟鹏,等.一种基于群体智能的客户行为分析算法[J].计算机学报,2003,26(8):913-918.
[14] YANG Qiang,LIU Yang,CHEN Tianjian,et al.Federated machine learning:concept and applications[J].ACM Transactions on Intelligent Systems and Technology,2019,10(2):1-19.
[15] FUCHS-KITTOWSKI F,FAUST D,BALOIAN N,et al.Architecture of mobile crowdsourcing systems[C]//Proceedings of CYTED-RITOS International Workshop on Groupware.Berlin,Germany:Springer,2014:121-136.
[16] PHUTTHARAK J,LOKE S W.A review of mobile crowdsourcing architectures and challenges:toward crowd-empowered internet-of-things[J].IEEE Access,2019,7:304-324.
[17] RA M R,LIU Bin,GOVINDAN R,et al.Medusa:a pro-gramming framework for crowd-sensing applications[C]//Proceedings of the 10th International Conference on Mobile System Application Services.New York,USA:ACM Press,2012:337-350.
[18] HU XIPING,CHU T H S,CHAN H C B,et al.Vita:a crowdsensing-oriented mobile cyber-physical system[J].IEEE Transactions on Emerging Topics Computing,2013,1(1):148-165.
[19] HUPFER S,MULLER M.MoCoMapps:mobile collaborative map-based applications[C]//Proceedings of CSCW'12.New York,USA:ACM Press,2012:43-44.
[20] DAS T,MOHAN P,PADMANABHAN V N,et al.PRISM:platform for remote sensing using smartphones[C]//Proceedings of International Conference on Mobile Systems,Applications and Services.New York,USA:ACM Press,2010:63-76.
[21] CORNELIUS C,KAPADIA A,KOTZ D,et al.AnonySense:privacy-aware people-centric sensing[EB/OL].[2019-09-24].http://www.doc88.com/p-9943363761284.html.
[22] RODRIGUES J,MARQUES E R B,LOPES L M B,et al.Towards a middleware for mobile edge-cloud applica-tions[C]//Proceedings of the 2nd Workshop on Middle-ware for Edge Clouds and Cloudlets.New York,USA:ACM Press,2017:1-10.
[23] YU Dunhui,ZHOU Zhuang,WANG Yi.Crowdsourcing software task assignment method for collaborative development[J].IEEE Access,2019,7:35743-35754.
[24] LAKHANI L R,GARVIN D A,LONSTEIN E.Topcoder(a):developing software through crowdsourcing[EB/OL].[2019-09-24].https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2002884.
[25] ZANATTA A L,MACHADO L S,PEREIRA G B,et al.Software crowdsourcing platforms[J].IEEE Software,2016,33(6):112-116.
[26] TONG Yongxin,YUAN Ye,CHENG Yurong,et al.Survey on spatiotemporal crowdsourced data management techniques[J].Journal of Software,2017,28(1):35-58.(in Chinese)童咏昕,袁野,成雨蓉,等.时空众包数据管理技术研究综述[J].软件学报,2017,28(1):35-58.
[27] URKULLU A,PÉREZ A,CALVO B.On the evaluation and selection of classifier learning algorithms with crowdsourced data[J].Applied Soft Computing,2019,80:832-844.
[28] TROLL J,BLOHM I,LEIMEISTER J M.Why incorporating a platform-intermediary can increase crowdsourcees' engage-ment[J].Business and Information Systems Engineering,2019,61:433-450.
[29] MUNKRES J.Algorithms for the assignment and transportation problems[J].Journal of the Society for Industrial and Applied Mathematics,1957,5(1):32-38.

选择文件类型/文献管理软件名称

选择包含的内容

群体智能2.0研究综述

Research Review of Crowd Intelligence 2.0

RichHTML

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	郑美光, 杨泳. 基于互信息软聚类的个性化联邦学习算法[J]. 计算机工程, 2023, 49(8): 20-28.
[2]	程适, 王雪萍, 刘悦, 史玉回. 面向非线性方程组的学习型头脑风暴优化算法[J]. 计算机工程, 2023, 49(7): 47-54.
[3]	温依霖, 赵乃良, 曾艳, 韩猛, 岳鲁鹏, 张纪林. 基于本地模型质量的客户端选择方法[J]. 计算机工程, 2023, 49(6): 131-143.
[4]	陈何雄, 罗宇薇, 韦云凯, 郭威, 杭菲璐, 何映军, 杨宁. 基于联邦学习的SDN异常流量协同检测技术[J]. 计算机工程, 2023, 49(3): 168-176.
[5]	李尤慧子, 俞海涛, 殷昱煜, 高洪皓. 基于超级账本的集群联邦优化模型[J]. 计算机工程, 2023, 49(1): 22-30.
[6]	刘金硕, 詹岱依, 邓娟, 王丽娜. 基于深度神经网络和联邦学习的网络入侵检测[J]. 计算机工程, 2023, 49(1): 15-21,30.
[7]	周全兴, 李秋贤, 丁红发, 樊玫玫. 基于博弈论优化的高效联邦学习方案[J]. 计算机工程, 2022, 48(8): 144-151,159.
[8]	王树芬, 张哲, 马士尧, 陈俞强, 伍一. 一种鲁棒的半监督联邦学习系统[J]. 计算机工程, 2022, 48(6): 107-114,123.
[9]	陈乃月, 金一, 李浥东, 蔡露鑫, 魏圆梦. 基于区块链的公平性联邦学习模型[J]. 计算机工程, 2022, 48(6): 33-41.
[10]	温亚兰, 陈美娟. 融合联邦学习与区块链的医疗数据共享方案[J]. 计算机工程, 2022, 48(5): 145-153,161.
[11]	柏财通, 崔翛龙, 李爱. 基于本地蒸馏联邦学习的鲁棒语音识别技术[J]. 计算机工程, 2022, 48(10): 103-109.
[12]	杨文琦, 章阳, 聂江天, 杨和林, 康嘉文, 熊泽辉. 基于联邦学习的无线网络节点能量与信息管理策略[J]. 计算机工程, 2022, 48(1): 188-196,203.
[13]	冯桫, 刘井平, 蒋海云, 肖仰华. 基于机器阅读理解模型与众包验证的属性值抽取方法[J]. 计算机工程, 2021, 47(5): 97-103.
[14]	王海, 翁晨傲, 李克, 骆曦. 一种面向基站扇区方向角估计的改进SVM算法[J]. 计算机工程, 2021, 47(4): 120-126.
[15]	邢虎, 陈荣, 唐文君. 基于预测算法的在线空间众包任务分配策略[J]. 计算机工程, 2020, 46(9): 27-34,43.

模态框（Modal）标题

选择文件类型/文献管理软件名称

选择包含的内容

群体智能2.0研究综述

Research Review of Crowd Intelligence 2.0

RichHTML

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价