Design and Development of Artificial Intelligence-Enabled IoT Framework for Satellite-Based Navigation Services
J. R. K. Kumar Dabbakuti, Rangababu Peesapati, Kiran Kumar Anumandla
Abstract
The advancement of Internet of Things (IoT) -based computing platforms open novel possibilities for exploring and leveraging Global Navigation Satellite Systems (GNSS). This work utilizes machine learning models and discusses the application of IoT scenarios for ionospheric monitoring and forecasting systems. A workflow discusses the effectiveness of the end-to-end solution in navigation applications through results obtained from the Successive Variational Mode–Decomposition– Kernel Extreme Learning Machine (SVMD–KELM) method, which reduces the need for expensive hardware and infrastructure. The proposed approach offers advantages over Variational Mode–Decomposition (VMD)-KELM in terms of computational efficiency and improved accuracy (Table II), making it a preferable choice for applications that require real-time analytics and reliable Global Positioning System–Total Electron Content (GPS–TEC) predictions. Further, the paper emphasizes two real-world scenarios: utilizing the LoRa network for near-distance communication and integrating the Amazon Web Services (AWS) cloud for longer-distance communication. The Framework allows efficient data acquisition and transmission, with a high success rate (99.7 %) in broadcasting GPS signal delay corrections. Finally, the paper proposes an integrated cloud-based terrestrial navigation system as a proof of concept for Machine-to-Machine (M2M) communication. The system offers a scalable solution for GNSS-based IoT applications, ensuring reliable navigation information even in challenging environments and meeting real-time GNSS/ Navigation with Indian Constellation (NavIC) user requirements.