Volume 4, Issue 3, May 2019, Page: 82-90
Abnormal Vehicle Load Identification Method Based on Genetic Algorithm and Wireless Sensor Network
Sorush Niknamian, Department of Military Medicine, Liberty University, Lynchburg, USA
Received: Mar. 13, 2019;       Accepted: Apr. 15, 2019;       Published: Jun. 26, 2019
DOI: 10.11648/j.ajtte.20190403.12      View  115      Downloads  31
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.
Keywords
Genetic Algorithm, Wireless Sensor Network Abnormal Vehicle Load Data, Recognition Method
To cite this article
Sorush Niknamian, Abnormal Vehicle Load Identification Method Based on Genetic Algorithm and Wireless Sensor Network, American Journal of Traffic and Transportation Engineering. Vol. 4, No. 3, 2019, pp. 82-90. doi: 10.11648/j.ajtte.20190403.12
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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