Paper Info
Title
IoT Sensing Parameters Adaptive Matching Algorithm.
Abstract
As the 'Industry 4.0' and 'Made in China 2025' has been put forward, the need of the large-scale system integration for Internet of Things (IoT) has been more and more urgent. At present, different IoT systems have different database types, table structures and denominating rules for sensing parameters. So for the existing IoT system integration, there are such as sensing parameter's conversion difficulty, complex matching process, low integrating efficiency issues. To solve these problems, we propose a novel model for IoT sensing parameter automatically matching which can achieve the IoT system integration on a large-scale. Meanwhile combining KNN thought, using a weighted method to improve the KNN algorithm, we put forward the automatic IoT sensing parameters matching algorithm. By the multiple practical IoT system integration cases, we validate the rationality and efficiency of the model and the algorithm. The result shows that the model and the algorithm are feasible and efficient. They realize the rapid automatic matching for the heterogeneous IoT sensing parameters, improving the IoT system's integration efficiency. It is conducive to the large-scale heterogeneous IoT system quick integration and has great significance to promote the IoT's application in large scale.
Year
DOI
Venue
2016
10.1007/978-3-319-42553-5_17
Lecture Notes in Computer Science
Keywords
Field
DocType
Internet of Things,KNN,System integration,Parameter matching
k-nearest neighbors algorithm,Data mining,Adaptive matching,Computer science,Internet of Things,Algorithm,Blossom algorithm,System integration
Conference
Volume
ISSN
Citations 
9784
0302-9743
1
PageRank 
References 
Authors
0.37
15
6
Name
Order
Citations
PageRank
Zhijin Qiu182.79
Naijun Hu210.37
Zhongwen Guo311613.32
Like Qiu411.04
Shuai Guo521.38
Xi Wang6113.98