STATE ANALYSIS INTERNET OF THINGS DEVICES BASED ON BAGGING

The development of IoT concept makes it necessary to search and improve models and methods for analyzing the state of remote autonomous devices. Due to the fact that some devices are located outside the controlled area, it becomes necessary to develop universal models and methods for identifying the state of low-power devices from a computational point of view, using complex approaches to analyzing data coming from various information channels. The article discusses an approach to identifying IoT devices state, based on parallel functioning classifiers that process time series received from elements in various states and modes of operation. The aim of the work is to develop an approach for identifying the state of IoT devices based on time series recorded during the execution of various processes. The proposed solution is based on methods of parallel classification and statistical analysis, requires an initial labeled sample. The use of several classifiers that give an answer "independently" from each other makes it possible to average the error by "collective" voting. The developed approach is tested on a sequence of classifying algorithms, to the input of which the time series obtained experimentally under various operating conditions were fed. Results are presented for a naive Bayesian classifier, decision trees, discriminant analysis, and the k nearest neighbors method. The use of a sequence of classification algorithms operating in parallel allows scaling by adding new classifiers without losing processing speed. The method makes it possible to identify the state of the Internet of Things device with relatively small requirements for computing resources, ease of implementation, and scalability by adding new classifying algorithms.