Developing the Fuzzy Logic Rules Based on Clustering Algorithms for Data Analysis in the IoT Network

The rapid development of digital communication systems led to the formation of a class of IoT systems that provide control of various production processes, in particular, traditional transport systems that must function in real-time. However, the computational complexity of data processing for the purpose of their analysis and decision-making can be significant, requiring finding a compromise between computational complexity and reducing the time of data processing directly. The solution to the problem of traffic forecasting in conditions of limited time and computing resources in the IoT system is possible due to the usage of the logical rules that can be developed from statistical data sets collected by the IoT system, and which are close to human perception. But these rules are unclear, because the limits of the parameter values changing, according to how they are built cannot be determined unequivocally. This paper proposes an approach to developing a fuzzy knowledge base using machine learning methods to reduce computational complexity in the decision-making process. The data processing scenario uses Fuzzy C-Means clustering, the KMeans++ algorithm for the cluster centers primary initialization, and the genetic algorithm for defining a set of fuzzy knowledge base rules. The F-score metric is used as a metric that evaluates the quality of the obtained fuzzy logical rules. The database of fuzzy logical rules can be used to analyze data in real-time systems to improve the performance and reliability of the decision-making process, significantly reducing the time of data analysis, which cannot be immediately interpreted unambiguously due to their significant volume. The proposed approach to a fuzzy knowledge base development made it possible to construct fuzzy logical rules from statistical datasets, create a fuzzy knowledge base, apply periodically the procedures for learning and retraining the set of fuzzy logical rules, and also achieve an assessment of the quality of the developed fuzzy logical rules using the example of the problem of predicting the traffic jams occurrence at the indicator level of the F-score = 0.875. In practice, this allows for more efficient use of road networks and resources, namely, the traffic on different routes distribution, planning the traffic lights working schedule, and other optimization measures to reduce congestion.