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 forec...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2023, Vol.61, p.1-12
Main Authors: Dabbakuti, J. R. K. Kumar, Peesapati, Rangababu, Anumandla, Kiran Kumar
Format: Article
Language:English
Subjects:
Amazon web services (AWS)
Artificial intelligence
Artificial neural networks
Cloud computing
Communication
Data acquisition
Data models
Decomposition
Distance
Forecasting
Global navigation satellite system
global navigation satellite system (GNSS)
Global positioning systems
GPS
Internet of Things
Internet of Things (IoT)
kernel extreme learning machine (KELM)
Learning algorithms
long range (LoRa)
Machine learning
Navigation
Navigation systems
Navigational satellites
Positioning systems
Real time
Real-time systems
Reliability
Satellite constellations
Signal delay
successive variational mode decomposition (SVMD)
System effectiveness
Total Electron Content
User requirements
Web services
Workflow
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Summary: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.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2023.3328858