False traffic information detection based on weak classifiers integration in vehicular ad hoc networks

Abstract

Vehicles report traffic information mutually by self-organized manner in vehicular ad hoc networks (VANET),and the message need to be identified in the open network environment.However,it is very difficult for fast moving ve-hicles to detect a lot of traffic alert information in a short time.To solve this problem,a false traffic message detection method was presented based on weak classifiers integration.Firstly,the effective features of traffic alert information was extended and segmentation rules were designed to divide the information feature set into multiple feature subsets,then the corresponding weak classifiers were used to process feature subsets respectively according to the different character-istics of the subsets' features.Simulation experiments and performance analysis show that the selected weak classifiers integration method reduces the detection time,and because of the application of combined features,the detection rate is better than the test of using only some of the characteristics.

Keywords

• VANET;false information detection;weak classifiers integration;BP neural network