Research on dynamic routing algorithm based on gaussian mixture model

In this paper, based on the method of environmental sound detection, a neural network model based on capsule network and Gaussian mixture model is proposed. The model proposed in this paper mainly aims at the disadvantages of dynamic routing algorithm in the capsule network, and proposes a dynamic routing algorithm based on Gaussian mixture model. The improved dynamic routing algorithm assumes that the characteristics of the data conform to the multi-dimensional Gaussian distribution, so the model can learn the distribution of data features by building distribution functions of different classes. The information entropy is used as the activation value of the salient degree of the feature. Through experiments, the accuracy of the proposed algorithm on Urbansound8K data set is more than 92%, which is 4.8% higher than the original algorithm.

A Boltzmann Machine Optimizing Dynamic Routing for FANETs

Routing optimization for FANETs is a kind of NP-hard in the field of combinatorial optimization that describes simple and difficult to handle. The quality of routing has a direct impact on the network quality of FANETs, and the design of routing protocols becomes a very challenging topic in FANETs. In this paper, we study the characteristics of dynamic routing, combine the characteristics of FANETs themselves, use the energy of nodes, bandwidth, link stability, etc. as the metric of routing, and use Boltzmann machine for routing search to form an optimized dynamic routing protocol. The NS3 simulation simulator is used to compare and study with traditional MANET dynamic routing AODV and DSR, and the simulation results show that the routes obtained by using Boltzmann machine search are better than AODV and DSR in many aspects such as end-to-end average delay, average route survival time and control overhead.