Propagation-Aware Knowledge Extraction for Fault Detection in Wireless Sensor Networks via RF Link-Quality, Text, and Data Mining
DOI:
https://doi.org/10.31838/NJAP/07.02.22Keywords:
Wireless sensor networks, RF propagation, Fault detection, Knowledge extraction, Data miningAbstract
Wireless sensor networks (WSNs) are the foundation of the data-driven environments in contemporary industry, agriculture, healthcare, and smart cities. However, early fault identification in WSNs is an ongoing problem because of severe propagation conditions, the uncertainty of environmental factors and unreliable sensor operation. In this article, the authors describe a state-of-the-art propagation-aware knowledge extraction framework to address robust fault detection through the combination of RF link-quality characterization, text mining in network logs, and scalable data-mining algorithms. The given approach integrates multi-source information such as physical-layer measurements, semantic event logs, real-time data streams into an adaptive decision engine. The system achieves significantly greater fault localization accuracy and responsiveness, because, using both advanced propagation modeling and machine learning, the system dynamically adapts to channel conditions and semantic context unlike the legacy methods. Decades of simulation and real-world sensor field results indicate that it can improve by more than 15 percent the detection accuracy, lowering the false negative rates and able to scale itself to a variety of propagation situations. Such an approach solves major shortcomings of the previous research and paves the way to robust, context-sensitive sensor platforms that are needed by the next-generation IoT and communication networks.
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