# Development of a Multi-Sensor GNSS-IoT System for Precise Water Surface Elevation Measurement
## 6 Conclusions  
In this paper, we develop an innovative multi-sensor GNSS-IoT system to estimate and monitor WSE. Our experimental results demonstrate the accuracy of our system in providing precise WSE measurements at Googong reservoir, Australia, validating the potential of low-cost GNSS technologies for hydrological monitoring. Over the four-month experimental period between February and May 2024, the system showcased its ability to deliver an accuracy of 7 mm for 6 h averaged WSE solutions and 28 mm for epoch-by-epoch WSE solutions using a short-baseline (\(\sim\)0.5 km) PPK processing method. The comparison between our data and reference data revealed strong consistency, particularly in sub-daily frequency measurements, affirming our system's capability to support the validation of satellite Earth observation data, such as those from the SWOT mission. The integration of multiple sensors, specifically the ultrasonic and accelerometer sensors, while primarily aimed at detecting large movements, contributed to the overall robustness of the system. The system's performance during various weather conditions, including heavy rainfall, indicated minimal impact on data quality, further enhancing its reliability for continuous monitoring. Future research should focus on optimizing the system's error budget by increasing the sampling frequency of the ultrasonic and accelerometer sensors to better resolve pontoon attitude and short-term vertical motion, thereby improving the accuracy of WSE estimation. Enhancing the integration of IoT-based tilt sensing and expanding deployments across a  
Figure 17: Device APC 2D variations. (**a**) East and north positioning; (**b**) 2D tracking. Green data is from before 18 May 2024. Blue data is from after 18 May 2024. -wide range of hydrological environments would also enable broader validation of the system's applicability and robustness. The development of advanced data processing algorithms, including machine learning techniques for real-time data analysis, could further improve the accuracy and usability of the system for both research and operational contexts. This will also enable the development of QC flags and anomaly detection mechanisms to support operational deployment. In conclusion, we have presented a significant advancement in low-cost GNSS-based WSE monitoring, offering a cost-effective and accurate solution for long-term hydrological studies and satellite Earth observation data validation. J.W. : conceptualization, methodology, software, formal analysis, writing--original draft preparation; M.C.G. : conceptualization, data acquisition, writing--review and editing; C.W. : methodology, investigation, writing--review; L.H. : funding and data acquisition, resources, project administration. All authors have read and agreed to the published version of the manuscript. This research was funded by the SmartSat CRC Queensland Earth Observation Hub (Project P6.08). Not applicable. The datasets generated and analysed during the current study are available from the corresponding author upon reasonable request. We thank CSIRO colleagues [PERSON] and [PERSON], and [PERSON] of Environmental Sensing Systems for supporting instrument deployment on the Dark-water Inland Observatory Network pontoon. We also thank [PERSON] and [PERSON] of Icon Water Limited for their support and for providing the validation data. The authors declare the following potential conflicts of interest: This study was funded by the SmartSat CRC Queensland Earth Observation Hub (Project P6.08), with Kurloo Technology Ltd. contributing 33% of the research funding. [PERSON], [PERSON] and [PERSON] are employees of Kurloo Technology Ltd., which has a commercial interest in the GNSS-IoT system described in this paper. However, the research presented in this paper has been conducted objectively, without influence from financial or competitive interests.