Artificial intelligence driven wireless network remote monitoring based on Diffie–Hellman parameter method

Abstract

Remote monitoring of wireless networks based on artificial intelligence can build sensitive anomaly recognition mechanisms, automated event analysis engines and accurate global operation and maintenance capabilities for wireless network defense. Firstly, a simple camera is used to realize real-time acquisition and wireless transmission of video signals based on software-based MPEG-4 compression coding method. The self-developed ActiveX control with video decoding function is embedded in the webpage to realize real-time dynamic display of video information in the computer browser of the monitoring terminal. Secondly, in order to realize the intelligent control of wireless network, the research based on the reverse motion degree posture planning is carried out. The DH parameter method is used to establish the linkage coordinate system of wireless network remote monitoring, and the kinematics formula is derived. The geometric analysis method is used to calculate the motion trajectory of remote monitoring, accurately locate the various angles of remote monitoring and obtain the best motion path. Based on the Pm angle control method of fuzzy neural network, the RBF neural network, fuzzy control the combination of control and Pm control, using the self-learning ability of the neural network and the fuzzy reasoning is ability of fuzzy control. The end effector was adjusted to the target position. Finally, the established mathematical model was simulated by MATLAB, and the characteristics of wireless network remote monitoring were verified.