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Assessing and Improving the Quality of IoT Sensor Data in Environmental Monitoring Networks: A Focus on Peatlands
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  • Nwamaka Okafor ,
  • Ruchita Ingle,
  • Matthew Saunders,
  • Declan Delaney
Nwamaka Okafor

Corresponding Author:[email protected]

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Ruchita Ingle
Matthew Saunders
Declan Delaney

Abstract

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 paper provides a long term evaluation of IoT sensors data quality in environmental monitoring networks, particularly focusing on peatland regions. IoT sensors have the capacity to provide high resolution spatiotemporal dataset in environmental monitoring networks. Sensor data quality plays significant role in increasing the adoption of IoT devices for environmental data gathering. However, due to the nature of deployment (i.e., in harsh and unfavourable weather conditions), coupled with the limitations of low-cost components, IoT sensors are prone to collection of erroneous data, also the nature of peatland ecosystems presents unique challenges in data quality assurance due to their complex and dynamic characteristics. This paper 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. To address these challenges, the paper presents and evaluates methods for improving data quality in peatland monitoring networks. These methods encompass advanced sensor calibration techniques, data validation algorithms, machine learning approaches, data processing and data fusion strategies.
23 Feb 2024Submitted to TechRxiv
27 Feb 2024Published in TechRxiv