Wireless Mesh Video Based on Neural Network in Cloud Edge Collaborative Management and Control Software

In the cloud edge collaborative management and control software, due to the large amount of wireless mesh video data and high requirements for real-time, packet loss is easy to occur in the transmission process. How to improve the transmission reliability of wireless mesh video has always been a research hotspot. Aiming at the problem that the reliability of data transmission in cloud edge collaborative management and control software is not high, this paper proposes a wireless mesh network load balancing protocol NNP based on neural network prediction model_L2MPM. The protocol takes the length of MAC interface queue as the measure of traffic load, and then uses RBF neural network prediction model to predict the traffic load of nodes in mesh network. In order to avoid the congestion in the next time, we can improve the performance of the network according to the load of the next node. The simulation results show that: the protocol uses RBF neural network prediction model to achieve the node traffic load prediction, and then achieves load balancing. Experiments show that nnp-l2mpm protocol has good performance.