An Integrated Machine Learning and IoT Architecture for Real-Time Data Analytics and Adaptive Decision Systems
Keywords:
Machine Learning, Internet of Things, Edge Computing, Real-Time AnalyticsAbstract
The convergence of Machine Learning (ML) and the Internet of Things (IoT) has led to the emergence of intelligent cyber-physical ecosystems capable of real-time analytics and autonomous decision-making. However, existing IoT architectures often suffer from limitations in scalability, latency, energy efficiency, and security, which restrict their ability to support adaptive intelligence in dynamic environments. This paper proposes an integrated ML-IoT architecture designed for real-time data analytics and adaptive decision systems across distributed environments. The study synthesizes advancements in edge intelligence, cloud computing, and secure IoT frameworks to design a layered architecture that enables efficient data processing, predictive analytics, and context-aware decision-making. Through systematic analysis of existing literature, the paper identifies key gaps in IoT-based ML integration, particularly in resource-constrained environments and security-sensitive applications. The proposed architecture emphasizes energy-efficient resource allocation, secure communication, and adaptive learning mechanisms to enhance system responsiveness and reliability. The findings indicate that combining ML algorithms with IoT infrastructures significantly improves decision accuracy, operational efficiency, and system scalability, while also introducing challenges related to interoperability and computational overhead.
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