Design a Gaussian mixture-based clustering model for enhancing accuracy and robustness in smart homes

Abstract

Nowadays, smart homes have become quite complicated systems. Thus, an appropriate technique for controlling all those devices is necessary, especially considering that certain nodes are likely to be broken. In that connection, we have proposed two algorithms related to Gaussian mixture models (GMM): GMM equal and GMM unequal. They were compared with graph neural network (GNN) equal, GNN unequal, and the LucasWheel algorithms. The peculiarity of the GMM equal algorithm consists in the fact that all clusters should have similar sizes and shapes, which is quite useful for routing and balancing purposes, while the clusters in the GMM unequal algorithm can have various sizes and shapes depending on the data distribution. All five models were analyzed using 843 nodes, where failure rates ranged from zero to fifty percent. The surprising outcome of this analysis is that GMM equal performed better than the other four models in every aspect. Efficiency was steady and steadily increased in accordance with the rising failure rate. The Wiener index gradually fell from its initial value to nearly zero, suggesting a dense connection among the nodes and an evenly spread-out network. Furthermore, GMM equal attained the highest modularity among the five models at every failure level. In combination, these results indicate that GMM equal is the most balanced topology, with the best balance between reliability, efficient communication, and scalability when applied to the internet of things and smart homes.