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

Sorush Niknamian

doi: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.

Highlights

With the extension of wireless sensor network applications in the field of vehicles, in-vehicle sensor networks, as a new generation of network technology that has attracted much attention, have broad application prospects in urban road condition monitoring and traffic anomaly detection
In order to solve the above problems, this paper proposes an accurate method for identifying abnormal vehicle load data in wireless sensor networks based on genetic algorithm
The vehicle load data are set up by the Lincoln Laboratory of Massachusetts Institute of Technology, and the TCP/IP vehicle load data are extracted from the typical American air force wireless sensor network. 41 characteristics are extracted from the vehicle load data

Summary

Introduction

With the extension of wireless sensor network applications in the field of vehicles, in-vehicle sensor networks, as a new generation of network technology that has attracted much attention, have broad application prospects in urban road condition monitoring and traffic anomaly detection. Reference [7] proposes a method for identifying abnormal vehicle load data in complex wireless sensor networks. In order to solve the above problems, this paper proposes an accurate method for identifying abnormal vehicle load data in wireless sensor networks based on genetic algorithm.

Results

Conclusion