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behavioral feature and Correlative Detection of Multiple Types of Node in the Internet of Vehicles

Computers, Materials & Continua 2020年第8期64卷 1127-1137页

作者： Pengshou Xie Guoqiang Ma Tao Feng Yan Yan Xueming Han School of Computer and Communications Lanzhou University of TechnologyLanzhou730050China Department of Computing Faculty of Science and EngineeringMacquarie UniversitySydneyNSW 2109Australia

Undoubtedly,uncooperative or malicious nodes threaten the safety of Internet of Vehicles(IoV)by destroying routing or *** 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 ***,it is difficult to effectively assess the trust level of a vehicle node only by message forwarding,data consistency,and energy *** order to resolve these problems,a novel mechanism and a new trust calculating model is proposed in this ***,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 ***,we conducted simulation experiments to verify the accuracy and time of this detection method under different speed-rate topological conditions of *** 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.

关键词： IoV behavioral feature double layer detection feature correlation analysis correlative detection model

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