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.