Methodology of adaptive data processing in IоT monitoring systems with multilevel sensor data filtering and self-tuning
Вантажиться...
Дата
2025
Автори
Науковий керівник
Назва журналу
Номер ISSN
Назва тому
Видавець
National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"
Анотація
The study focuses on the processes of collecting and preprocessing heterogeneous sensor data. The aim of the research is to develop a method of adaptive filtering and automatic trigger adjustment that ensures stable operation of IoT monitoring systems in the presence of noise, impulse outliers, and seasonal fluctuations. A methodology for adaptive data processing is proposed, combining multi-level data filtering with automatic self-adjustment of control thresholds in monitoring systems. This approach not only improves the accuracy of real-time sensor measurements but also dynamically adapts the monitoring system parameters to changing operating conditions, thereby minimizing the number of false incidents. Within the study, a model of multi-level filtering was formalized, based on a median filter, a moving-average filter, and an exponential smoothing method. The use of a multi-level filter provides comprehensive data cleansing, stabilization of time series, and extraction of key trends. A mechanism for automatic adjustment of control thresholds in the Zabbix monitoring system was developed, where threshold values are determined based on statistical parameters and trends identified at the multi-level filtering stage. This mechanism integrates into the subsequent data-processing pipeline, ensuring that the system automatically accounts for daily, seasonal, and other fluctuations of the dynamic data-collection environment. Experimental studies involving various types of sensors confirmed improved measurement accuracy and a significant reduction in false alerts in the monitoring system. In particular, humidity-measurement accuracy improved by an average of 6.52%, while impulse temperature spikes were reduced by 53.06%. Compared to traditional approaches, the proposed methodology provides higher noise resilience and adaptability to changing environmental conditions, making it an effective solution for industrial, environmental, and other real-time IoT systems.
Опис
Ключові слова
IoT, sensor measurements, adaptive control thresholds, self-adjustment, monitoring, Zabbix, seasonal adaptation, сенсорнi вимiрювання, адаптивнi пороги контролю, самоналаштування, монiторинг, сезонна адаптацiя.
Бібліографічний опис
Haidai, A. Methodology of adaptive data processing in IоT monitoring systems with multilevel sensor data filtering and self-tuning / Anatolii Haidai, Iryna Klymenko // Information, Computing and Intelligent systems. – 2025. – No. 7. – P. 110-126. – Bibliogr.: 10 ref.