CONTEXT-AWARE RELIABILITY ASSESSMENT IN IoT SYSTEMS
DOI:
https://doi.org/10.17721/AIT.2025.2.01Keywords:
Internet of Things, node reliability, context-aware model, computer simulation, distributed systems, reliability assessment.Abstract
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Background. The paper addresses the problem of reliability assessment of nodes in distributed Internet of Things (IoT) systems operating in highly dynamic environments. The use of classical statistical reliability models and structural approaches without considering environmental and operational context leads to reduced accuracy of failure prediction and limited adaptability of IoT infrastructures. The aim of the paper is to develop a context-aware reliability assessment method for distributed IoT systems that improves the accuracy of node state evaluation under changing conditions.
Methods. The research is based on methods of mathematical modeling, complex systems analysis, computer simulation, numerical data processing, and graph-based representation of network structures.
Results. To achieve the stated goal, the following tasks were accomplished: development of a context-dependent reliability model that integrates physical, network, and energy-related parameters; design of a normalization procedure for heterogeneous context variables; construction of sensitivity functions for different impact factors; formation of an integral reliability index using weighted aggregation; software implementation of the proposed method in Python using NumPy, pandas, and networkx libraries; and experimental verification through simulation of distributed IoT environments. The obtained results demonstrate higher reliability estimation accuracy of the proposed method compared to traditional Mean Time To Failure approaches and context-independent Markov models.
Conclusions. The developed context-aware method provides an adaptive reliability evaluation mechanism suitable for distributed IoT environments and supports real-time monitoring and decision-making tasks. The method can be utilized in IoT system diagnostics, failure prediction, and resource management subsystems.
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Copyright (c) 2026 Олена СІПКО, канд. техн. наук, Ольга КРАВЧЕНКО, канд. техн. наук, доц. (Автор)
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