Advances and Challenges in IoT Sensors Data Handling and Processing in Environmental Monitoring Networks

Abstract

Advances in IoT technologies provide 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 monitoring air quality, to monitoring biodiverse regions  including forests and peatlands to protecting endangered species. The use of IoT sensor devices in environmental monitoring, however,  has raised several questions, especially pertaining to the quality of sensor data, reliability, accuracy, and in-field performance. IoT sensors  are prone to failures and errors especially when deployed for medium to longer-term. 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 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. 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 surveys the various methods for  handling and processing IoT sensor data in environmental monitoring networks, the prospects, challenges, and limitations of these  methods are examined.