In the process of indoor positioning and navigation, a Bayesian network-based floor localization algorithm was proposed for the problem of large error of floor localization when only the pedestrian height displacement considered. Firstly, Extended Kalman Filter (EKF) was adopted to calculate the vertical displacement of the pedestrian by fusing inertial sensor data and barometer data. Then, the acceleration integral features after error compensation was used to detect the corner when the pedestrian went upstairs or downstairs. Finally, Bayesian network was introduced to locate the pedestrian on the most likely floor based on the fusion of walking height and corner information. Experimental results show that, compared with the floor localization algorithm based on height displacement, the proposed algorithm has improved the accuracy of floor localization by 6.81%; and compared with the detection algorithm based on platform, the proposed algorithm has improved the accuracy of floor localization by 14.51%. In addition, the proposed algorithm achieves the accuracy of floor localization by 99.36% in the total 1247 times floor changing experiments.

In Software Defined Network (SDN), Dynamic Host Configuration Protocol (DHCP) flood attack packets can actively enter the controller in reactive mode, which causes a huge hazard to SDN. Aiming at the promblem that the traditional defense method against DHCP flood attack cannot keep the SDN network from control link blocking caused by the attack, a Dynamic Defense Mechanism (DDM) against DHCP flood attacks was proposed. DDM is composed of a detection model and mitigation model. In the detection model, different from the static threshold detection method, a dynamic peak estimation model was constructed by two key parameters - DHCP average traffic seed and IP pool surplus to evaluate whether the ports were attacked. If the ports were attacked, the mitigation model would be informed. In the mitigation model, the IP pool cleaning was performed based on the response character of Address Resolution Protocol (ARP), and an interval interception mechanism was designed to intercept the attack source, mitigating the congestion and minimizing the impact on users during interception. Simulation experimental results show that the detection error of DDM is averagely 18.75%, lower than that of the static threshold detection. The DDM mitigation model can effectively intercept traffic and reduce the waiting time for users to access the network during the interception by an average of 81.45%.

In the traditional indoor pedestrian positioning algorithm based on dead reckoning and Kalman filtering, there is a problem of cumulative error in the heading angle, which makes the positional error continue to accumulate continuously. To solve this problem, a pedestrian heading particle filter algorithm with indoor environment constraints was proposed to correct direction error. Firstly, the indoor map information was abstracted into a structure represented by line segments, and the map data was dynamically integrated into the mechanism of particle compensation and weight allocation. Then, the heading self-correction mechanism was constructed through the correlation map data and the sample to be calibrated. Finally, the distance weighting mechanism was constructed through correlation map data and particle placement. In addition, the particle filter model was simplified, and heading was used as the only state variable to optimize. And while improving the positioning accuracy, the dimension of state vector was reduced, thereby the complexity of data analysis and processing was reduced. Through the integration of indoor environmental information, the proposed algorithm can effectively suppress the continuous accumulation of directional errors. The experimental results show that, compared with the traditional Kalman filter algorithm, the proposed algorithm can significantly improve the pedestrian positioning accuracy and stability. In the two-dimensional walking experiment with a distance of 435 m, the heading angle error is reduced from 15.3° to 0.9°, and the absolute error at the end position is reduced from 5.50 m to 0.87 m.

Concerning the low accuracy of identifying relevant Chinese events, a correlation recognition algorithm for Chinese news events based on Grey Relational Analysis (GRA) was proposed, which is a multiple factor analysis method. Firstly, three factors that affect the event correlation, including co-occurrence of triggers, shared nouns between events and the similarity of the event sentences, were proposed through analyzing the characteristics of Chinese news events. Secondly, the three factors were quantified and the influence weights of them were calculated. Finally, GRA was used to combine the three factors, and the GRA model between events was established to realize event correlation recognition. The experimental results show that the three factors for event correlation recognition are effective, and compared with the method only using one influence factor, the proposed algorithm improves the accuracy of event correlation recognition.