A low-cost, IoT-enabled GNSS-based ionospheric monitoring system with real-time data access

Ionospheric irregularities affect the propagation of Global Navigation Satellite System (GNSS) signals, often leading to positioning errors, signal degradation, and occasional loss of lock. Although research-grade ionospheric monitoring stations provide highly accurate measurements, their cost and fixed infrastructure limit large-scale deployment and real-time accessibility. To address this gap, this paper presents the development and field validation of IonoAPI, a low-cost, Internet of Things (IoT)-enabled GNSS-based ionospheric monitoring platform designed for continuous and autonomous operation. The system integrates a dual-frequency u-blox ZED-F9P receiver with a Raspberry Pi-based processing unit. Raw GNSS observables recorded at 1 Hz are processed through a structured Python pipeline to estimate satellite geometry (elevation and azimuth), Dilution of Precision (DOP), total amplitude scintillation index ( S 4 ) derived from C / N 0 measurements, and uncalibrated relative slant Total Electron Content (STEC) using dual-frequency pseudorange combinations. To enhance data reliability, post-processing includes missing-data filtering, a 15° elevation mask, and statistical outlier detection prior to cloud transmission. Processed results are automatically uploaded to an Amazon Web Services Relational Database Service (AWS RDS) database via Node-RED and visualized through Grafana dashboards, while a Flask-based REST API enables structured programmatic access. The system operates in a near–real-time batch mode with an overall end-to-end latency of approximately 3–5 min. Validation was conducted through simultaneous observations with a co-located Septentrio PolaRx5 receiver. The platform was validated through simultaneous observations with a co-located Septentrio PolaRx5 receiver. Quantitative evaluation was performed using correlation coefficient ( r ), root mean square error (RMSE), mean absolute error (MAE), bias, normalized RMSE (NRMSE), and mean absolute percentage error (MAPE). Satellite geometry parameters showed near-unity correlation with minimal error, while the total S 4 values showed moderate agreement under the nominal daytime ionospheric conditions considered in this study. The uncalibrated relative STEC estimates followed the same temporal pattern, although a nearly constant offset was present due to the absence of Differential Code Bias (DCB) correction. After removal of the mean bias, differential STEC variations remained bounded without long-term drift, indicating reliable tracking of ionospheric variability. These results demonstrate that, although absolute calibration is not performed, the low-cost IonoAPI architecture reliably captures relative ionospheric dynamics and satellite geometry behavior. The integrated cloud-based framework supports scalable, distributed, and cost-effective ionospheric monitoring for research and space-weather applications.