Design and Development of Artificial Intelligence-Enabled IoT Framework for Satellite-Based Navigation Services

The advancement of Internet of Things (IoT)-based computing platforms opens novel possibilities for exploring and leveraging global navigation satellite systems (GNSS). This work utilizes machine-learning (ML) 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, making it a preferable choice for applications that require real-time analytics and reliable global positioning system-total electron content (GPS-TEC) predictions. Furthermore, this article emphasizes two real-world scenarios: utilizing the long-range (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, this article 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.