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

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.

Oblique shock train motion based on schlieren image processing

In this paper,shock train motion in a Mach number 2.7 duct is studied experimentally,and large numbers of schlieren images are obtained by a high-speed camera.An image processing method based on Maximum Correlation Detection(MCD)is proposed to detect shock train motion from the schlieren images,based on which the key structures,e.g.,separation positions and separation shock angles on the top and bottom walls,can be analysed in detail.The oscillations of the shock train are generated by rhombus and ellipse shafts at various excitation frequencies.According to the analysis of MCD results,the distributions of the frequency components of shock train oscillation generated by the two shafts are distinctly different,in which the motion generated by the ellipse shaft is much smoother;shock train motion is mainly characterized by the oscillation of separation position while the separation shock strength is not so sensitive to downstream disturbance;there is a hysteresis loop relation between the downstream pressure and separation position.