Integrating IBM Cloud Object Storage and Azure Stream Analytics for Scalable IoT Solutions Using Mesh Networks and Hidden Markov Models

Abstract

Background The emergence of the Internet of Things (IoT) generates large quantities of data and calls for scalable storage and real-time analytics technologies for the complexity of IoT in the smart city, healthcare, etc., industries. Methods This paper presents an approach for the analysis of IoT real-time data, using IBM Cloud Object Storage, Azure Stream Analytics, mesh networks, and Hidden Markov Models (HMMs). Objectives The main objective of this study is to enhance the scalability, efficiency, and predictive analytics of IoT systems by integrating cloud-based storage, real-time analytics with high throughput, and decentralized mesh networks for improved performance. Results The proposed method was 92% accurate, 90% efficient, and 93% scalable, outperforming current methods through increased real-time framing, reliable networking, and improved predictive power via HMMs. Conclusion This hybrid method serves as a scalable and deployable solution to any IoT system that is advantageous for real-time applications in smart cities, healthcare, and industrial automation.