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Showing posts with the label Topology-Driven Fault Detection in Smart Cities

Topology-Driven Fault Detection in Smart Cities

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Topology-Driven Fault Detection in Smart Cities Topology-Driven Fault Detection in Smart Cities: Utilizing Dynamic Graph Filtrations and Persistent Homology for IoT Connectivity Patterns Shrishti Rastogi / Research Article Abstract: The rapid proliferation of IoT devices in smart cities necessitates robust, real-time monitoring systems to detect infrastructure faults. Traditional graph-theoretic methods often fail to capture multi-scale structural changes under fluctuating environmental conditions. This paper presents a topology-driven framework utilizing Topological Data Analysis (TDA), specifically persistent homology, to analyze time-varying graph filtrations of IoT connectivity patterns. We integrate Distance-to-Measure (DTM) filtrations to mitigate noise, coupled with machine learning classifiers applied to stable topological summaries. Empirical evaluation on simulated smart city datasets demonstrates superior F1-s...
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Topology-Driven Fault Detection in Smart Cities

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Topology-Driven Fault Detection in Smart Cities Topology-Driven Fault Detection in Smart Cities: Utilizing Dynamic Graph Filtrations and Persistent Homology for IoT Connectivity Patterns Shrishti Rastogi / Research Article Abstract: The rapid proliferation of IoT devices in smart cities necessitates robust, real-time monitoring systems to detect infrastructure faults. Traditional graph-theoretic methods often fail to capture multi-scale structural changes under fluctuating environmental conditions. This paper presents a topology-driven framework utilizing Topological Data Analysis (TDA), specifically persistent homology, to analyze time-varying graph filtrations of IoT connectivity patterns. We integrate Distance-to-Measure (DTM) filtrations to mitigate noise, coupled with machine learning classifiers applied to stable topological summaries. Empirical evaluation on simulated smart city datasets demonstrates superior F1-s...
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