Dynamic Data Fusion Strategies using Machine Learning in IoT Systems
Abstract
The proliferation of Internet of Things (IoT) devices has led to the generation of vast and heterogeneous data streams from diverse sensors and sources. The effective fusion and integration of this dynamic and multi-modal data pose significant challenges for extracting meaningful insights and making informed decisions. This paper explores the utilization of Machine Learning (ML) techniques for dynamic data fusion in IoT systems to address these challenges.
In this study, we survey various data fusion strategies and their integration with ML algorithms to adaptively amalgamate disparate data sources in real-time. The paper highlights the significance of employing ML-driven approaches to handle the complexities associated with the variability, volume, and velocity of IoT-generated data. Moreover, it presents a comprehensive analysis of different ML models such as deep learning, ensemble methods, and probabilistic graphical models applied to data fusion tasks within IoT ecosystems.
Furthermore, the paper delves into the advantages and limitations of these ML-based data fusion strategies, addressing issues related to scalability, latency, and accuracy. It also discusses the implications of employing these approaches in diverse IoT applications, including smart cities, healthcare, industrial automation, and environmental monitoring.
The findings synthesized in this research aim to provide a roadmap for implementing dynamic data fusion techniques using ML in IoT systems. The insights and methodologies presented herein contribute to enhancing decision-making processes, enabling predictive analytics, and fostering advancements in various IoT-driven domains.
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