American International Journal of Advanced Research and Development

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IoT-Driven Data Analytics for Smart Decision Support

Author(s) Chao Wang
Country United States
Abstract The rapid expansion of Internet of Things (IoT) technologies has generated large volumes of real-time data from interconnected sensors, devices, machines, infrastructures, and digital platforms. IoT-driven data analytics provides an important mechanism for transforming these heterogeneous data streams into actionable information for smart decision support. This paper examines the role of IoT-driven data analytics in supporting intelligent and data-informed decisions across industrial, urban, healthcare, energy, transportation, logistics, and organizational environments. A conceptual and literature-based methodology is adopted to examine IoT data collection, preprocessing, storage, analytics, artificial intelligence, edge computing, cloud computing, visualization, and decision-support mechanisms. The analysis demonstrates that integrating IoT with advanced analytics can strengthen real-time monitoring, anomaly detection, prediction, resource optimization, operational coordination, and strategic decision-making.
However, the effectiveness of IoT-driven analytics is influenced by data quality, interoperability, cybersecurity, privacy, computational requirements, network reliability, scalability, and analytical complexity. The study concludes that intelligent decision support requires an integrated architecture connecting reliable data acquisition with appropriate analytical models and decision interfaces. Future research should emphasize real-time edge analytics, explainable artificial intelligence, federated learning, predictive decision support, privacy-preserving analytics, digital twins, and adaptive IoT architectures capable of responding to dynamic operational conditions.
Keywords Internet of Things, Data Analytics, Smart Decision Support, IoT Data, Artificial Intelligence, Big Data Analytics, Edge Computing, Cloud Computing, Predictive Analytics.
Field Engineering
Published In Volume 4, Issue 4, July-August 2022
Published On 2022-08-04
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