SEQUENCE AND SOFTWARE FOR SIMULATION-BASED EVALUATING THE PROBABILITY OF FAILURE-FREE OPERATION FOR WIRELESS SENSOR NETWORKS CONSIDERING VARIOUS SCENARIOS OF FAILURES

Abstract

Wireless sensor networks (WSNs) have become an essential component of contemporary monitoring, control, and data-gathering infrastructures. They are widely deployed in environmental observation, industrial automation, agricultural monitoring, and smart-city applications. Despite their extensive use, WSNs remain highly susceptible to stochastic failures caused by hardware degradation, energy depletion, environmental impacts, and malfunctions of peripheral components. Such failures may lead to decreased network availability, reduced quality of collected data, and, ultimately, diminished reliability of the entire system. Addressing these issues requires tools that allow researchers and engineers to analyze failure mechanisms under diverse conditions and to evaluate how different types of faults influence overall network behavior.

The article introduces a structured methodology and a dedicated simulation software tool designed to model, visualize, and assess the reliability of WSNs under a wide range of failure scenarios. The tool incorporates stochastic modeling based on exponential, normal, and Weibull distributions, enabling users to investigate both sudden and progressive sensor degradation. It also supports several failure-detection and assessment criteria, including threshold-based events, spatially correlated failures, and failures of external or peripheral modules. A key feature of the software is the ability to perform repeated simulation runs to obtain statistically significant results, allowing users to estimate reliability indicators such as mean time to failure, failure probability over time, and the impact of localized malfunctions on global network performance.

The examples presented in the article demonstrate that the developed tool can serve as an effective research instrument for analyzing robustness, designing fault-tolerant architectures, and validating reliability-enhancing algorithms for WSNs. Future work will focus on improving computational efficiency, expanding the library of failure models, integrating energy-aware behavior, and applying the simulator to more complex and large-scale network topologies.