An IoT-integrated SVMD-RVFL framework for ionospheric monitoring toward enhanced GNSS navigation applications

Abstract Total electron content (TEC) is a important parameter in the domains of space weather studies and Global Navigation Satellite System (GNSS)-based navigation and communication applications. Conventional linear forecasting models face difficulties in effectively representing the complex nonlinear behaviors of the ionospheric dynamics. On the other hand, nonlinear approaches derived from advanced learning methods offer higher accuracy, but they necessitate substantial computational resources, building them impractical for real-time use in resource-constrained IoT environments. The emergence of Internet of Things (IoT) technology has facilitated the accessibility of affordable GNSS data associated through cloud platforms, allowing for ongoing and instantaneous collection of TEC data. In this paper, an efficient Successive Variational Mode Decomposition (SVMD) and Random Vector Functional Link (RVFL) framework is implemented to predict TEC via cloud platforms through Think Speak channels. The TEC observations from the year 2018 at Bengaluru (Geographic: 13.02° N, 77.57° E) is consider for analysis. The SVMD adaptively decomposes the TEC signal without requiring predefined mode selection, while RVFL enables fast training using random weights, direct connections, and universal approximation capabilities. The proposed model was evaluated using GNSS data from Bengaluru (13.02° N, 77.57° E). The results demonstrate that the SVMD–RVFL has an accuracy of 0.55 TECU for Root Mean Square Error (RMSE), 0.61 TECU for Mean Absolute Error (MAE), 7.64 % for Mean Absolute Percentage Error (MAPE), a correlation coefficient of 99.32 % and a training time of 3.82 s. The proposed approach demonstrates high precision and a low computational load, making it suitable for real-time ionospheric monitoring systems and IoT technologies.