Year: 2024
Type: conference-paper
Abstract: Methods for predicting possible failures in smart home systems and analyzing the data required for this have been considered in the study. A study of machine learning methods has been carried out: their features, advantages, and disadvantages have been identified, the metrics of each method have been studied, and the effectiveness of methods for predicting failures in a smart home has been established. It has been found that the Long Short-Term Memory (LSTM) model is distinguished by its ability to work with data sequences and store information for a long time. The characteristics of the LSTM method and its algorithm have been studied in detail. The study emphasizes the importance of collecting and processing various data, such as sensor data, energy consumption, and information about devices and users. The results of the study can be useful for the further development of smart home control systems to improve their reliability and efficiency.
Language: English
Topic: IoT-based Smart Home Systems
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W7166326024