Assessing and Improving IoT Sensor Data Quality in Environmental Monitoring Networks: A Focus on Peatlands

Advances in Internet of Things (IoT) technologies have resulted in a significant surge in the utilization of sensor devices across diverse domains for environmental sensing and monitoring. The applications of IoT sensor devices in environmental monitoring span a wide range, including the surveillance of biodiverse areas, such as peatlands, forests, and oceans, as well as air quality monitoring, commercial agriculture, and the safeguarding of endangered species. This article provides a long-term evaluation of IoT sensors data quality in environmental monitoring networks, particularly focusing on peatlands. IoT sensors have the capacity to provide high-resolution spatiotemporal data set in environmental monitoring networks. Sensor data quality plays a significant role in increasing the adoption of IoT devices for environmental data gathering. However, logistics challenges (i.e., in harsh and unfavorable weather conditions) along with low-cost components limitations add on to the data collection errors. This article identifies specific challenges and issues related to IoT sensor data quality in different peatland ecotopes. These challenges include sensor placement and calibration, data validation and fusion, environmental interference, and the management of data gaps and uncertainties. This research work evaluates methods for improving data quality in peatland monitoring network by encompassing advanced sensor calibration techniques, data validation algorithms, machine learning approaches, data processing, and data fusion strategies.