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.

Electrical crosstalk-coupling measurement and analysis for digital closed loop fibre optic gyro

The phase modulation and the closed-loop controller can generate electrical crosstalk-coupling in digital closed- loop fibre optic gyro. Four electrical cross-coupling paths are verified by the open-loop testing approach. It is found the variation of ramp amplitude will lead to the alternation of gyro bias. The amplitude and the phase parameters of the electrical crosstalk signal are measured by lock-in amplifier, and the variation of gyro bias is confirmed to be caused by the alternation of phase according to the amplitude of the ramp. A digital closed-loop fibre optic gyro electrical crosstalk-coupling model is built by approximating the electrical cross-coupling paths as a proportion and integration segment. The results of simulation and experiment show that the modulation signal electrical crosstalk-coupling can cause the dead zone of the gyro when a small angular velocity is inputted, and it could also lead to a periodic vibration of the bias error of the gyro when a large angular velocity is inputted.

Efficient Activation Method of Hardware Trojan Based on Greedy Algorithm

To generate test vector sets that can efficiently activate hardware Trojans and improve probability of the hardware Trojan activation,an efficient hardware Trojan activation method is proposed based on greedy algorithm for combinatorial hardware Trojans. Based on the greedy algorithm and the recursive construction method in the combination test,the method formulates appropriate and useful greedy strategy and generates test vector sets with different combinatorial correlation coefficients to activate hardware Trojans in target circuits. The experiment was carried out based on advanced encryption standard（ AES） hardware encryption circuit,different combinatorial hardware Trojans were implanted in AES as target circuits,the experiment of detecting hardware Trojans in target circuits was performed by applying the proposed method and different combinatorial hardware Trojans in target circuits were activated successfully many times in the experiment. The experimental results showthat the test vector sets generated using the proposed method could effectively activate combinatorial hardware Trojans,improve the probability of the hardware Trojan being activated,and also be applied to practice.

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.