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.

IoT sensors are becoming increasingly important supplement to traditional monitoring systems, particularly for in-situ based monitoring. However, data collection based on IoT sensors are often plagued with missing values usually occurring as a result of sensor faults, network failures, drifts and other operational issues.

IoT sensors are becoming increasingly important supplement to traditional monitoring systems, particularly for in-situ based monitoring. Data collected using IoT sensors are often plagued with missing values occurring as a result of sensor faults, network failures, drifts and other operational issues. Missing data can have substantial impact on in-field sensor calibration methods. The goal of this research is to achieve effective calibration of sensors in the context of such missing data. To this end, two objectives are presented in this paper. 1) Identify and examine effective imputation strategy for missing data in IoT sensors. 2) Determine sensor calibration performance using calibration techniques on data set with imputed values. Specifically, this paper examines the performance of Variational Autoencoder (VAE), Neural Network with Random Weights (NNRW), Multiple Imputation by Chain Equations (MICE), Random forest based imputation (missForest) and K-Nearest Neighbour (KNN) for imputation of missing values on IoT sensors. Furthermore, the performance of sensor calibration via different supervised algorithms trained on the imputed dataset were evaluated. The analysis showed that VAE technique outperforms the others in imputing the missing values at different proportions of missingness on two real-world datasets. Experimental results also showed improved calibration performance with imputed dataset.

Advances in IoT technologies provide a new epoch in ecological sensing leading to the deployment of millions of sensor devices to sense and monitor the environment. IoT sensors have the capacity to provide high spatial and temporal resolution data to supplement traditional data-gathering methods, thereby filling the gaps that exist within current environmental data-gathering methods.  Applications of IoT sensors in environmental monitoring are broad ranging from air quality monitoring, and monitoring of biodiverse regions including forests and peatlands to protection of endangered species. The use of IoT sensor devices in environmental monitoring, however, has raised several questions, especially pertaining to the quality of sensor data, sensor reliability,  accuracy, and in-field performance. IoT sensors are prone to failures especially when deployed for medium to longer-term monitoring, leading to the collection of erroneous data. A common question within the IoT research domain is how to handle IoT sensor data, especially in terms of processing, fusion with other data sources as well and analysis to glean useful insights from the data in support of effective decision-making. Several authors have proposed different data handling methods for IoT sensor data and proposed techniques have led to improvement in overall data quality and field performance of IoT sensor devices. Methods for addressing IoT sensor data analysis integration with emerging technologies, such as cloud computing, fog computing, and edge computing along with methods to make Data storage choices have also been proposed. This paper will survey the various methods that have been designed and developed for handling and processing IoT sensor data, especially in environmental monitoring networks, the prospects, challenges, and limitations of these methods will be examined.

When equipped with a reliable calibration model, Low-Cost Sensor (LCS) can be relied upon as an effective option for gas concentration estimation, providing robust and high spatio-temporal resolution data to replace large-scale analytical instruments. In this paper, we present ProxySense, a rapid and efficient approach for gas concentration estimation. The ProxySense pipeline consists of gas sensing unit made up of array of metal oxide LCS, data pre-processing including an effective approach based on Variatioanl Autoencoders (VAE) for handling missing sensor data and Long Short Term Memory Reccurrent Neural Network (LSTM-RNN) prediction model. We investigate the capability of ProxySense in exploiting the deep characteristics that exist in multi-sensors’ responses to predict the concentration of a gas for which no specific sensor is included in a multi-sensor device. We evaluated ProxySense for benzene (C6H6) and carbon monoxide (CO) concentration predictions and compared the performances to multiple baselines by means of prediction error characterization. We further studied the relationship between model performance and training length and showed ProxySense to be highly accurate for gas concentration prediction even for small number of training period.