Proceedings of the 2015 4th International Conference on Sensors, Measurement and Intelligent Materials

The research of mobile sensor nodes scheduling optimization based on GA-AFSA

Authors
Xiao-Feng Zhang, Li-Juan Yu, Wen-Wu Mao, Cheng-Ming Chen, Jun Xia
Corresponding Author
Xiao-Feng Zhang
Available Online January 2016.
DOI
https://doi.org/10.2991/icsmim-15.2016.68How to use a DOI?
Keywords
WSN; Genetic algorithm; Artificial fish algorithm; sensor nodes Scheduling
Abstract

in order to make up for sensor nodes’ death effectively, this paper studies a kind of artificial fish algorithm (AFSA) improved by genetic algorithm (GA) to optimize the project of mobile sensor nodes Scheduling and the coverage of Wireless Sensor Networks (WSN) and energy consumption will be as the optimization goal . This paper introduce crossover and mutation probability of GA to improve AFSA. The simulation experiments have proved that The global search ability of GA-AFSA is better, and the improved GA-AFSA has higher efficiency and quality of the algorithm.

Copyright
© 2016, 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 2015 4th International Conference on Sensors, Measurement and Intelligent Materials
Series
Advances in Computer Science Research
Publication Date
January 2016
ISBN
978-94-6252-157-5
ISSN
2352-538X
DOI
https://doi.org/10.2991/icsmim-15.2016.68How to use a DOI?
Copyright
© 2016, 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  - Xiao-Feng Zhang
AU  - Li-Juan Yu
AU  - Wen-Wu Mao
AU  - Cheng-Ming Chen
AU  - Jun Xia
PY  - 2016/01
DA  - 2016/01
TI  - The research of mobile sensor nodes scheduling optimization based on GA-AFSA
BT  - Proceedings of the 2015 4th International Conference on Sensors, Measurement and Intelligent Materials
PB  - Atlantis Press
SP  - 364
EP  - 369
SN  - 2352-538X
UR  - https://doi.org/10.2991/icsmim-15.2016.68
DO  - https://doi.org/10.2991/icsmim-15.2016.68
ID  - Zhang2016/01
ER  -