Network Public Opinion Prediction and Control Based on Edge Computing and Artificial Intelligence New Paradigm

Algorithm 1

New paradigm algorithm for adaptive artificial intelligence based on edge computing.

Initialize compute nodes in the network, corresponding user characteristics, network estimation parameters, and their cumulative context.

For each time segment , 2..., , do the following.

According to the network parameters of node , combined with the current user characteristics (), the corresponding cost is evaluated according to Thompson sampling.

The computing node with the least estimated cost is selected as the current service placement strategy, and the corresponding QoS performance is received at the end of the time segment.

Update and select the corresponding user characteristics of the compute node and its network estimation parameters.