Journal of Cloud Computing and Internet of Things ISSN: 3107-4391 (Online)

Authors: Sneha Patel, Amit Joshi

Abstract: The proliferation of Internet of Things (IoT) devices in cloud environments has led to massive data generation, necessitating efficient data management strategies. This paper investigates the role of machine learning algorithms in optimizing data storage, processing, and retrieval in cloud-based IoT systems. Through predictive analytics and adaptive learning techniques, machine learning can improve data management efficiency, reducing latency and energy consumption. We analyze supervised, unsupervised, and reinforcement learning approaches for their applicability in cloud-IoT ecosystems and evaluate their performance in real-time data processing scenarios. The proposed solutions demonstrate significant improvements in system throughput and resource optimization.

Keywords: Machine Learning, Data Management, Cloud-IoT Systems, Predictive Analytics, Resource Optimization