Localization of multiple jamming attackers in vehicular ad hoc network

In vehicular ad hoc network, wireless jamming attacks are easy to be launched in the control channel and can cause serious influence on the network performance which may cause further safety accidents. In order to address the issue of wireless jamming attacks, a new technique which localizes the jamming attackers and prevents vehicles from jamming through human intervention is proposed. In this article, we propose a range-free approach to localize the source of the attacker and determine the number of jamming attackers. The data set is the locating information and the jamming detection information associated with each vehicle. Then, we formulate the problem of determining the number of attackers as a multiclass detection problem. We define the incorrectly classified area and use it to measure the distance between samples and centroids in fuzzy c-means algorithm. We further determine the number of jamming attackers through the coverage rate of beaconing circles and utilize weight-based fuzzy c-means to classify the data set. When the data set is classified as acceptable, we further explore the means of using particle swarm optimization algorithm to calculate the positional coordinates of each attacker. We simulate our techniques in MATLAB, and both urban traffic area and open area are considered in our simulation. The experimental results suggest that the proposed algorithm can achieve high precision when determining the number of attackers while the result of the classified performance is always satisfying. Our localization results lead to higher accuracy than other existing solutions. Also, when the data set is limited, the chances of taking accurate localization are higher than other measures.