Behavioral Feature and Correlative Detection of Multiple Types of Node in the Internet of Vehicles |
| |
Authors: | Pengshou Xie Guoqiang Ma Tao Feng Yan Yan Xueming Han |
| |
Affiliation: | 1.School of Computer and Communications, Lanzhou University of Technology, Lanzhou, 730050, China.
2 Department of Computing, Faculty of Science and Engineering, Macquarie University, Sydney, NSW
2109, Australia. |
| |
Abstract: | Undoubtedly, uncooperative or malicious nodes threaten the safety of Internet
of Vehicles (IoV) by destroying routing or data. To this end, some researchers have
designed some node detection mechanisms and trust calculating algorithms based on
some different feature parameters of IoV such as communication, data, energy, etc., to
detect and evaluate vehicle nodes. However, it is difficult to effectively assess the trust
level of a vehicle node only by message forwarding, data consistency, and energy
sufficiency. In order to resolve these problems, a novel mechanism and a new trust
calculating model is proposed in this paper. First, the four tuple method is adopted, to
qualitatively describing various types of nodes of IoV; Second, analyzing the behavioral
features and correlation of various nodes based on route forwarding rate, data forwarding
rate and physical location; third, designing double layer detection feature parameters with
the ability to detect uncooperative nodes and malicious nodes; fourth, establishing a node
correlative detection model with a double layer structure by combining the network layer
and the perception layer. Accordingly, we conducted simulation experiments to verify the
accuracy and time of this detection method under different speed-rate topological
conditions of IoV. The results show that comparing with methods which only considers
energy or communication parameters, the method proposed in this paper has obvious
advantages in the detection of uncooperative and malicious nodes of IoV; especially, with
the double detection feature parameters and node correlative detection model combined,
detection accuracy is effectively improved, and the calculation time of node detection is
largely reduced. |
| |
Keywords: | IoV behavioral feature double layer detection feature correlation analysis correlative detection model |
|
| 点击此处可从《》浏览原始摘要信息 |
|
点击此处可从《》下载全文 |
|