Abnormal Vehicle Load Identification Method Based on Genetic Algorithm and Wireless Sensor Network

Sorush Niknamian

doi:doi:10.11648/j.ajtte.20190403.12

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Abnormal Vehicle Load Identification Method Based on Genetic Algorithm and Wireless Sensor Network

Sorush Niknamian

Published in American Journal of Traffic and Transportation Engineering (Volume 4, Issue 3)

Received: 13 March 2019 Accepted: 15 April 2019 Published: 26 June 2019

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Abstract

Wireless sensor network refers to a group of spatially dispersed and dedicated sensors for monitoring and recording the physical conditions of the environment and organizing the collected data at a central location. The current abnormal wireless sensor network vehicle load data recognition method is more complex, which leads to low recognition rate, false alarm rate and slow recognition speed. Based on the genetic algorithm, the accurate method for abnormal wireless sensor network vehicle load data recognition is proposed. The effective feature set of abnormal vehicle load data in the wireless sensor network is constructed, to remove irrelevant features and redundant features from existing abnormal wireless sensor network vehicle load data. The abnormal wireless sensor network vehicle load data in the effective feature set are coded, to reduce the recognition time of abnormal wireless sensor network vehicle load data. The adaptive fitness function, crossover operator and mutation operator are applied to genetic algorithm, which can improve the recognition rate, reduce the false alarm rate, and realize the recognition of abnormal vehicle load data wireless sensor network. The experimental results show that the recognition rate of this method is high, the false alarm rate is low, and the time of recognition is less.

Published in	American Journal of Traffic and Transportation Engineering (Volume 4, Issue 3)
DOI	10.11648/j.ajtte.20190403.12
Page(s)	82-90
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Genetic Algorithm, Wireless Sensor Network Abnormal Vehicle Load Data, Recognition Method

References

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APA Style

Sorush Niknamian. (2019). Abnormal Vehicle Load Identification Method Based on Genetic Algorithm and Wireless Sensor Network. American Journal of Traffic and Transportation Engineering, 4(3), 82-90. https://doi.org/10.11648/j.ajtte.20190403.12

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ACS Style

Sorush Niknamian. Abnormal Vehicle Load Identification Method Based on Genetic Algorithm and Wireless Sensor Network. Am. J. Traffic Transp. Eng. 2019, 4(3), 82-90. doi: 10.11648/j.ajtte.20190403.12

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AMA Style

Sorush Niknamian. Abnormal Vehicle Load Identification Method Based on Genetic Algorithm and Wireless Sensor Network. Am J Traffic Transp Eng. 2019;4(3):82-90. doi: 10.11648/j.ajtte.20190403.12

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@article{10.11648/j.ajtte.20190403.12,
  author = {Sorush Niknamian},
  title = {Abnormal Vehicle Load Identification Method Based on Genetic Algorithm and Wireless Sensor Network},
  journal = {American Journal of Traffic and Transportation Engineering},
  volume = {4},
  number = {3},
  pages = {82-90},
  doi = {10.11648/j.ajtte.20190403.12},
  url = {https://doi.org/10.11648/j.ajtte.20190403.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtte.20190403.12},
  abstract = {Wireless sensor network refers to a group of spatially dispersed and dedicated sensors for monitoring and recording the physical conditions of the environment and organizing the collected data at a central location. The current abnormal wireless sensor network vehicle load data recognition method is more complex, which leads to low recognition rate, false alarm rate and slow recognition speed. Based on the genetic algorithm, the accurate method for abnormal wireless sensor network vehicle load data recognition is proposed. The effective feature set of abnormal vehicle load data in the wireless sensor network is constructed, to remove irrelevant features and redundant features from existing abnormal wireless sensor network vehicle load data. The abnormal wireless sensor network vehicle load data in the effective feature set are coded, to reduce the recognition time of abnormal wireless sensor network vehicle load data. The adaptive fitness function, crossover operator and mutation operator are applied to genetic algorithm, which can improve the recognition rate, reduce the false alarm rate, and realize the recognition of abnormal vehicle load data wireless sensor network. The experimental results show that the recognition rate of this method is high, the false alarm rate is low, and the time of recognition is less.},
 year = {2019}
}

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TY  - JOUR
T1  - Abnormal Vehicle Load Identification Method Based on Genetic Algorithm and Wireless Sensor Network
AU  - Sorush Niknamian
Y1  - 2019/06/26
PY  - 2019
N1  - https://doi.org/10.11648/j.ajtte.20190403.12
DO  - 10.11648/j.ajtte.20190403.12
T2  - American Journal of Traffic and Transportation Engineering
JF  - American Journal of Traffic and Transportation Engineering
JO  - American Journal of Traffic and Transportation Engineering
SP  - 82
EP  - 90
PB  - Science Publishing Group
SN  - 2578-8604
UR  - https://doi.org/10.11648/j.ajtte.20190403.12
AB  - Wireless sensor network refers to a group of spatially dispersed and dedicated sensors for monitoring and recording the physical conditions of the environment and organizing the collected data at a central location. The current abnormal wireless sensor network vehicle load data recognition method is more complex, which leads to low recognition rate, false alarm rate and slow recognition speed. Based on the genetic algorithm, the accurate method for abnormal wireless sensor network vehicle load data recognition is proposed. The effective feature set of abnormal vehicle load data in the wireless sensor network is constructed, to remove irrelevant features and redundant features from existing abnormal wireless sensor network vehicle load data. The abnormal wireless sensor network vehicle load data in the effective feature set are coded, to reduce the recognition time of abnormal wireless sensor network vehicle load data. The adaptive fitness function, crossover operator and mutation operator are applied to genetic algorithm, which can improve the recognition rate, reduce the false alarm rate, and realize the recognition of abnormal vehicle load data wireless sensor network. The experimental results show that the recognition rate of this method is high, the false alarm rate is low, and the time of recognition is less.
VL  - 4
IS  - 3
ER  -

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Author Information

Sorush Niknamian

Department of Military Medicine, Liberty University, Lynchburg, USA

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APA Style

Sorush Niknamian. (2019). Abnormal Vehicle Load Identification Method Based on Genetic Algorithm and Wireless Sensor Network. American Journal of Traffic and Transportation Engineering, 4(3), 82-90. https://doi.org/10.11648/j.ajtte.20190403.12

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ACS Style

Sorush Niknamian. Abnormal Vehicle Load Identification Method Based on Genetic Algorithm and Wireless Sensor Network. Am. J. Traffic Transp. Eng. 2019, 4(3), 82-90. doi: 10.11648/j.ajtte.20190403.12

Copy | Download

AMA Style

Sorush Niknamian. Abnormal Vehicle Load Identification Method Based on Genetic Algorithm and Wireless Sensor Network. Am J Traffic Transp Eng. 2019;4(3):82-90. doi: 10.11648/j.ajtte.20190403.12

Copy | Download

@article{10.11648/j.ajtte.20190403.12,
  author = {Sorush Niknamian},
  title = {Abnormal Vehicle Load Identification Method Based on Genetic Algorithm and Wireless Sensor Network},
  journal = {American Journal of Traffic and Transportation Engineering},
  volume = {4},
  number = {3},
  pages = {82-90},
  doi = {10.11648/j.ajtte.20190403.12},
  url = {https://doi.org/10.11648/j.ajtte.20190403.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtte.20190403.12},
  abstract = {Wireless sensor network refers to a group of spatially dispersed and dedicated sensors for monitoring and recording the physical conditions of the environment and organizing the collected data at a central location. The current abnormal wireless sensor network vehicle load data recognition method is more complex, which leads to low recognition rate, false alarm rate and slow recognition speed. Based on the genetic algorithm, the accurate method for abnormal wireless sensor network vehicle load data recognition is proposed. The effective feature set of abnormal vehicle load data in the wireless sensor network is constructed, to remove irrelevant features and redundant features from existing abnormal wireless sensor network vehicle load data. The abnormal wireless sensor network vehicle load data in the effective feature set are coded, to reduce the recognition time of abnormal wireless sensor network vehicle load data. The adaptive fitness function, crossover operator and mutation operator are applied to genetic algorithm, which can improve the recognition rate, reduce the false alarm rate, and realize the recognition of abnormal vehicle load data wireless sensor network. The experimental results show that the recognition rate of this method is high, the false alarm rate is low, and the time of recognition is less.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Abnormal Vehicle Load Identification Method Based on Genetic Algorithm and Wireless Sensor Network
AU  - Sorush Niknamian
Y1  - 2019/06/26
PY  - 2019
N1  - https://doi.org/10.11648/j.ajtte.20190403.12
DO  - 10.11648/j.ajtte.20190403.12
T2  - American Journal of Traffic and Transportation Engineering
JF  - American Journal of Traffic and Transportation Engineering
JO  - American Journal of Traffic and Transportation Engineering
SP  - 82
EP  - 90
PB  - Science Publishing Group
SN  - 2578-8604
UR  - https://doi.org/10.11648/j.ajtte.20190403.12
AB  - Wireless sensor network refers to a group of spatially dispersed and dedicated sensors for monitoring and recording the physical conditions of the environment and organizing the collected data at a central location. The current abnormal wireless sensor network vehicle load data recognition method is more complex, which leads to low recognition rate, false alarm rate and slow recognition speed. Based on the genetic algorithm, the accurate method for abnormal wireless sensor network vehicle load data recognition is proposed. The effective feature set of abnormal vehicle load data in the wireless sensor network is constructed, to remove irrelevant features and redundant features from existing abnormal wireless sensor network vehicle load data. The abnormal wireless sensor network vehicle load data in the effective feature set are coded, to reduce the recognition time of abnormal wireless sensor network vehicle load data. The adaptive fitness function, crossover operator and mutation operator are applied to genetic algorithm, which can improve the recognition rate, reduce the false alarm rate, and realize the recognition of abnormal vehicle load data wireless sensor network. The experimental results show that the recognition rate of this method is high, the false alarm rate is low, and the time of recognition is less.
VL  - 4
IS  - 3
ER  -

Copy | Download