Keywords: remote monitoring, object condition, heterogeneous data source, information process, structural-functional model, data quality control, temporal synchronization, window processing, robust notifications, traceability of results
UDC 004.4'2
DOI: 10.26102/2310-6018/2026.55.4.006
Abstract. The paper proposes a modification of the information process model for remote monitoring of object condition aimed at improving the correctness of result interpretation under conditions of heterogeneous data sources, different measurement frequencies, data transmission delays, and incomplete observations. The objective of the study is to extend the original model by incorporating additional stages and mechanisms that ensure data quality control, temporal alignment of data streams, robustness of notifications, and reproducibility of the obtained assessments. The research methods include structural and functional decomposition of the information process and formalization of data processing principles at each added stage. The proposed modification introduces: an object profile serving as a context for parameter interpretation and a mechanism for unambiguous assignment of measurements to a specific object; temporal synchronization of data streams based on window processing; a data quality control loop with validity labeling and anomaly detection; a confidence indicator for state assessment considering the completeness and quality of observations; event-based interpretation of results; robust notification mechanisms based on an extended threshold model with hysteresis and message rate limiting; explainable inference tools identifying the parameters that influenced the assigned status; and traceability of results through logging of input data, interpretation rules, and output assessments. As a result, a refined structure of the information process has been developed, enabling state assessment that accounts for the quality and consistency of input data and ensuring stable delivery of results to the monitoring subject.
1. Zhang X., Zhang T., Wang G., et al. Remote sensing object detection meets deep learning: A meta-review of challenges and advances. arXiv. URL: https://arxiv.org/abs/2309.06751 [Accessed 16th January 2026].
2. Hoeser Th., Bachofer F., Kuenzer C. Object detection and image segmentation with deep learning on earth observation data: A review – Part II: Applications. Remote Sensing. 2020;12(18). https://doi.org/10.3390/rs12183053
3. Janga Bh., Asamani G.P., Sun Z., Cristea N. A review of practical AI for remote sensing in earth sciences. Remote Sensing. 2023;15(16). https://doi.org/10.3390/rs15164112
4. Ye P. Remote sensing approaches for meteorological disaster monitoring: Recent achievements and new challenges. International Journal of Environmental Research and Public Health. 2022;19(6). https://doi.org/10.3390/ijerph19063701
5. Hajduczok A.G., Muallem S.N., Nudy M.S., DeWaters A.L., Boehmer J.P. Remote monitoring for heart failure using implantable devices: A systematic review, meta-analysis, and meta-regression of randomized controlled trials. Heart Failure Reviews. 2022;27(4):1281–1300. https://doi.org/10.1007/s10741-021-10150-5
6. Sirotina A.S., Kobyakova O.S., Deev I.A., et al. Remote health monitoring: Global and domestic experience. Social Aspects of Population Health. 2022;68(2). (In Russ.). URL: http://vestnik.mednet.ru/content/view/1355/30/lang/ru/
7. Shaderkin I.A. Remote monitoring of human health and the environment: opportunities and limitations. Russian Journal of Telemedicine and E-Health. 2022;8(3):45–54. (In Russ.). https://doi.org/10.29188/2712-9217-2022-8-3-45-54
8. Fedorov V.F., Stolyar V.L. Personal telemedicine. Prospects for implementation. Medical Doctor and IT. 2020;(2):36–44. (In Russ.). https://doi.org/10.37690/1811-0193-2020-2-36-44
9. Gilka V.V., Kuznetsova A.S., Moldovskaya A.A., El-Ait Ja.F. Verification of the model and method functionality for remote health monitoring illustrated by the deviations in human body temperature indicators. Izvestiya SFedU. Engineering Sciences. 2023;(5):127–137. (In Russ.). https://doi.org/10.18522/2311-3103-2023-5-127-137
10. Gilka V.V., Kuznetsova A.S. Testing the operability of the remote monitoring method implemented in HelpMe-Tracker on people and checking the application's response to deviations in health indicators. Herald of Dagestan State Technical University. Technical Sciences. 2023;50(2):48–57. (In Russ.). https://doi.org/10.21822/2073-6185-2023-50-2-48-57
Keywords: remote monitoring, object condition, heterogeneous data source, information process, structural-functional model, data quality control, temporal synchronization, window processing, robust notifications, traceability of results
For citation: Gilka V.V., Kuznetsova A.S., Kachanov Y.A., Morozov D.A., Lomakin A.S. Modification of the information process model for remote monitoring of object condition based on heterogeneous data sources. Modeling, Optimization and Information Technology. 2026;14(4). URL: https://moitvivt.ru/ru/journal/article?id=2237 DOI: 10.26102/2310-6018/2026.55.4.006 (In Russ).
© Gilka V.V., Kuznetsova A.S., Kachanov Y.A., Morozov D.A., Lomakin A.S. Статья опубликована на условиях лицензии Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NS 4.0)Received 18.02.2026
Revised 06.04.2026
Accepted 19.04.2026