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Abnormal Vehicle Load Identification Method Based on Genetic Algorithm and Wireless Sensor Network
  • Sorush Niknamian
Sorush Niknamian
Liberty University

Corresponding Author:[email protected]

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Abstract

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.
2019Published in American Journal of Traffic and Transportation Engineering volume 4 issue 3 on pages 82. 10.11648/j.ajtte.20190403.12