Open Access

ARTICLE

Behavioral Feature and Correlative Detection of Multiple Types of Node in the Internet of Vehicles

Pengshou Xie¹, Guoqiang Ma^{1, *}, Tao Feng¹, Yan Yan^{1, 2}, Xueming Han¹

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.

* Corresponding Author: Guoqiang Ma. Email: .

Computers, Materials & Continua 2020, 64(2), 1127-1137. https://doi.org/10.32604/cmc.2020.09695

Received 15 January 2020; Accepted 17 April 2020; Issue published 10 June 2020

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.

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Citations