Decision Intelligence Architecture for Cloud IoT and Software Defined Networks using Fate Transport Models and Real Time Data Analytics
DOI:
https://doi.org/10.15662/IJEETR.2021.0306009Keywords:
Decision Intelligence, Cloud IoT, Software-Defined Networking (SDN), Fate–Transport Modeling, Real-Time Data Mining, Predictive Analytics, Cyber-Physical Systems, Network Optimization, Environmental Modeling, Adaptive Cloud ArchitectureAbstract
The convergence of Cloud Computing, Internet of Things (IoT), and Software-Defined Networking (SDN) has enabled highly dynamic and data-intensive digital ecosystems. However, effective decision-making in such environments requires advanced modeling techniques capable of handling environmental dynamics, network variability, and large-scale real-time data streams. This research proposes a Decision Intelligence Framework for Cloud IoT and Software-Defined Networks integrating fate–transport modeling with real-time data mining techniques. Fate–transport modeling is employed to simulate the movement, transformation, and accumulation of physical or digital entities across distributed systems, while real-time data mining extracts actionable insights from high-velocity IoT data streams. The framework incorporates adaptive SDN control mechanisms for optimized routing, intelligent resource allocation, and secure communication management. Cloud-based analytics engines perform predictive modeling, anomaly detection, and risk assessment to support agile decision-making. Experimental evaluation demonstrates improved response time, optimized network utilization, enhanced predictive accuracy, and reduced environmental or system risk compared to traditional rule-based architectures. The proposed framework contributes an integrated, scalable, and intelligent solution for complex cyber-physical environments, supporting applications in environmental monitoring, smart infrastructure, industrial systems, and critical network management.
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