Intelligent Data-Intensive loT: A Survey

被引:0
|
作者
Xiao, Bin [1 ]
Rahmani, Rahim [1 ]
Li, Yuhong [2 ]
Gillblad, Daniel [3 ]
Kanter, Theo [1 ]
机构
[1] Stockholm Univ, Dept Comp & Syst Sci, Stockholm, Sweden
[2] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing, Peoples R China
[3] Swedish Inst Comp Sci, Stockholm, Sweden
来源
2016 2ND IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATIONS (ICCC) | 2016年
关键词
intelligence enabler; data provision; internet of things; data-intensive; context; BIG-DATA; INTERNET; THINGS; ARCHITECTURE; FRAMEWORK; SUPPORT;
D O I
暂无
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
The loT paradigm proposes to connect entities intelligently with massive heterogeneous nature, which forms an ocean of devices and data whose complexity and volume are incremental with time. Different from the general big data or loT, the data-intensive feature of loT introduces several specific challenges, such as circumstance dynamicity and uncertainties. Hence, intelligence techniques are needed in solving the problems brought by the data intensity. Until recent, there are many different views to handle loT data and different intelligence enablers for loT, with different contributions and different targets. However, there are still some issues have not been considered. This paper will provide a fresh survey study on the data-intensive loT issue. Besides that, we conclude some shadow issues that have not been emphasized, which are interesting for the future. We propose also an extended big data model for intelligent data-intensive loT to tackle the challenges.
引用
收藏
页码:2362 / 2368
页数:7
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