Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017)

Research on Hierarchical Algorithm of Wireless Sensor Network Based on Small World Network Model

Authors
Qichao Tang
Corresponding Author
Qichao Tang
Available Online April 2017.
DOI
10.2991/fmsmt-17.2017.153How to use a DOI?
Keywords
Hierarchical algorithm, Wireless sensor network, Small world network model
Abstract

The characteristics of the small world network is widely used in Wireless Sensor Networks. In this paper, Hierarchical Algorithm of Wireless Sensor Network Based on Small World Network Model (HASWNM) is proposed. Firstly,the algorithm divide the monitoring area into different sub-regions, and select the cluster node according to the data transmission reliability.Secondly, build shortcuts between cluster heads according to the model of DAS .Finally,we found that the WSNs had small average path length and large clustering coefficient, and lifecycle of network is obviously improved .

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/).

Download article (PDF)

Volume Title
Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017)
Series
Advances in Engineering Research
Publication Date
April 2017
ISBN
10.2991/fmsmt-17.2017.153
ISSN
2352-5401
DOI
10.2991/fmsmt-17.2017.153How 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  - Qichao Tang
PY  - 2017/04
DA  - 2017/04
TI  - Research on Hierarchical Algorithm of Wireless Sensor Network Based on Small World Network Model
BT  - Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017)
PB  - Atlantis Press
SP  - 796
EP  - 800
SN  - 2352-5401
UR  - https://doi.org/10.2991/fmsmt-17.2017.153
DO  - 10.2991/fmsmt-17.2017.153
ID  - Tang2017/04
ER  -