Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016)

The Characteristics of Compound Models Designed for Internet of Things

Authors
Jing SU, Fei MA, Bing YAO
Corresponding Author
Jing SU
Available Online December 2016.
DOI
10.2991/cnct-16.2017.4How to use a DOI?
Keywords
Power law, 2-operator, Scale-free, Cumulative distribution, Average degree
Abstract

In order to study the scale-free of more and more real-life networks, we design, construct a class of compound network models by the methods of graph theory for understand and try to simulate network models from Internet of Things (IoT), and we computed the parameters of models, such as: average degree, operation distribution, clustering coefficients, diameters. In the article we have verified the scale-free nature of the compound network models, that is to say, if a scale-free network M(t) as base, the new network model N(t) have built through the network operation on the basis of M(t), it is still a scale-free network.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016)
Series
Advances in Computer Science Research
Publication Date
December 2016
ISBN
978-94-6252-301-2
ISSN
2352-538X
DOI
10.2991/cnct-16.2017.4How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Jing SU
AU  - Fei MA
AU  - Bing YAO
PY  - 2016/12
DA  - 2016/12
TI  - The Characteristics of Compound Models Designed for Internet of Things
BT  - Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016)
PB  - Atlantis Press
SP  - 23
EP  - 29
SN  - 2352-538X
UR  - https://doi.org/10.2991/cnct-16.2017.4
DO  - 10.2991/cnct-16.2017.4
ID  - SU2016/12
ER  -