Data visualization and pattern discovery in IoT
The fast multiplication of Internet of Things (IoT) ecosystems has led to huge amounts of hetero-geneous, high-dimensional, and dynamic data, which are difficult to analyze and make decisions. Traditional linear visualization and analysis tools are also not always suitable to show the nonlinear corr...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , , |
| التنسيق: | article |
| منشور في: |
2025
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| الوصول للمادة أونلاين: | http://hdl.handle.net/11675/14500 https: |
| الوسوم: |
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| الملخص: | The fast multiplication of Internet of Things (IoT) ecosystems has led to huge amounts of hetero-geneous, high-dimensional, and dynamic data, which are difficult to analyze and make decisions. Traditional linear visualization and analysis tools are also not always suitable to show the nonlinear correlations, latent dependencies, and changing patterns in IoT data. This paper attempts to fill this gap by presenting a holistic nonlinear optimization and artificial intelligence (AI)-supported framework of IoT data visualization and pattern discovery. The given method is a combination of nonlinear optimization of features with the help of metaheuristic algorithms and sophisticated dimensionality reduction techniques to preserve the important information with reducing redundancy. The knowledge, anomalies, and predictive trends in sensor networks are then extracted, detected, and identified using AI-driven models such as deep neural networks, graph neural networks, and reinforcement learning agents. An interpretable visualization layer that is trained on manifold learning methods like UMAP is more interpretable since the optimized feature spaces are then mapped to low-dimensional human-readable visual representations. The framework is proven by case-studies of smart agriculture and industrial IoT that prove the framework effective in optimization of irrigation schemes, enhancing crop yield forecasting, facilitating early fault detection, and minimizing downtime in production systems. The results of experiments indicate that it is more accurate, separates clusters better and that it is less complex to compute in comparison to the conventional methods to linear analysis like PCA and k-means clustering. The results highlight the disruptive nature of AI-enhanced nonlinear optimization to fill the gap between raw IoT data and actionable knowledge and thus provide scalable, interpretable, and intelligent analytics to next-generation IoT enabled applications. |
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