基于端和云的大规模上下文管理框架的研究与实现

计算机科学 ›› 2013, Vol. 40 ›› Issue (6): 84-89.

基于端和云的大规模上下文管理框架的研究与实现

史殿习,吴振东,丁博

国防科学技术大学计算机学院长沙410073;国防科学技术大学计算机学院长沙410073;国防科学技术大学计算机学院长沙410073

出版日期:2018-11-16 发布日期:2018-11-16
基金资助:
本文受国家核高基重大专项课题(2011ZX03002-004-01)资助

Research and Implementation of Large-scale Context Management Framework Based on Terminal and Cloud

SHI Dian-xi,WU Zhen-dong and DING Bo

Online:2018-11-16 Published:2018-11-16

摘要/Abstract

摘要： 上下文态势是将大规模、广地域范围内的上下文信息综合在一起形成的一种全局信息。随着各类具备感知能力的移动终端的普及,如何获取这种全局态势并利用态势来为用户提供更好的服务是亟待解决的问题。基于“端+云”相结合的计算模式,提出移动终端的统一抽象模型来实现上下文信息收集,进而提出了在云端对大规模上下文信息进行聚合、基于MapReduce计算模型的态势信息获取算法。通过一个大规模上下文管理框架对研究内容进行验证,并以一个交通态势实例验证了框架的有效性。

关键词: 上下文态势,软件抽象模型,聚合,MapReduce,基于规则

Abstract: The context situation refers the global view extracted from massive context information collected from a wide area．Along with the popularity of various mobile terminals with the ability to sense its context,it is a great problem to get this kind of situation and provide better service based on what we get．On the basis of the “terminal＋cloud” computing paradigm,this paper proposed a unified abstract model for mobile terminals to realize context collections．And then,we proposed an algorithm to realize the aggregation of massive context information on the cloud side,which is based on the MapReduce computing pattern．We validated the research content of this paper by a large-scale context management framework as well as a traffic situation application based on this framework.

Key words: Context situation,Software abstract model,Aggregation,MapReduce,Rule-based

史殿习,吴振东,丁博. 基于端和云的大规模上下文管理框架的研究与实现[J]. 计算机科学, 2013, 40(6): 84-89. https://doi.org/

SHI Dian-xi,WU Zhen-dong and DING Bo. Research and Implementation of Large-scale Context Management Framework Based on Terminal and Cloud[J]. Computer Science, 2013, 40(6): 84-89. https://doi.org/

参考文献

[1] Schilit B,Adams N,Want R．Context-Aware Computing Applications[C]∥Proceedings of Workshop on Mobile Computing Systems and Applications．1994
[2] Choudhury T,et al．The Mobile Sensing Platform:An Embed- ded System for Activity Recognition[J]．IEEE Pervasive Comp,2008,7(2):32-41
[3] Lane N D,Miluzzo E,Lu Hong,et al.A Survey of Mobile Phone Sensing[J]．Communications Magazine,IEEE,2010,8(9):140-150
[4] MapReduce．http://en.wikipedia.org/wiki/MapReduce,2011
[5] Dartmouth College．Mobile Sensing Group．http://sensorlab.cs.dartmouth.edu/
[6] Mun M,et al.Peir,the Personal Environmental Impact Report,as a Platform for Participatory Sensing Systems Research[C]∥Proc．7th ACM MobiSys．2009:55-68
[7] Thiagarajan A,et al.VTrack:Accurate,Energy-Aware TrafficDelay Estimation Using Mobile Phones[C]∥Proc．7th ACM SenSys．Berkeley,CA,Nov．2009
[8] UC Berkeley/Nokia/NAVTEQ．Mobile Millennium．ht-tp://traffic.berkeley.edu/
[9] witter Storm．http://www.infoq.com/news/2011/09/twitter-storm-real-time-hadoop,2011
[10] Ding Bo,Wang Huai-min,Shi Dian-xi．Pervasive middlewaretechnology[J]．计算机科学与探索,2007(3)
[11] Yau S,Karim F,Wang Y,et al.Reconfigurable context-sensitive middleware for pervasive computing[J]．Pervasive Computing,2002,1(3):33-40
[12] Da T,Zhang Q．A middleware for building context- aware mobile services[C]∥Vehicular Technology Conference,2004． VTC 2004-Spring.2004IEEE 59th,2004,5:2656-2660
[13] Korpipaa P,Koskinen M,Peltola J,et al．Bayesian approach to sensor-based context awareness[J]．Personal and Ubiquitous Computing,2003,7(2):113-124
[14] Schmidt A,Aidoo K A,Takaluoma A,et al.Advanced Interaction in Context[C]∥Handheld and Ubiquitous Computing．Springer Berlin Heidelberg,1999:89-101
[15] Castro P,Munz R．Managing context data for smart spaces[J]．Personal Communications,2000,7(5):44-46

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