# The Design and Deployment of an End-To-End IoT Infrastructure for the Natural Environment
## 6 Discussion
### Scientific Contributions  
Arguably the biggest success of the project was that we provided a working and fully functional Environmental IoT that enabled new modes of scientific discovery. The key to this was the deployment of a wide range of sensors covering hydrology, soil science, and animal behaviour. This enabled a new kind of integrative scientific enquiry, and also by bringing the data together, this also brought the associated scientific communities together to collaboratively understand the interdependencies in the complex environmental catchment under consideration. This acts as a beacon for future integrative and collaborative science in this area. The examination of this multi-faceted data across different geographic areas and (importantly) at different scales also enables this broader and deeper understanding of a catchment in all its complexities. The more obvious but equally profound contribution is providing real-time data to scientists. As mentioned in related work, environmental and earth sciences are overly reliant on data logging which require trips out to (often remote and inaccessible) locations to download data. The accessibility of rich, real-time data therefore represents a real paradigm shift for this area  
Figure 19: The complementarity of IoT, cloud, and data science is a key field of future work for integrative research efforts each needing to understand the role and capabilities of the others.  
and a major opportunity to support improved response to issues such as, for example, extreme events and natural disasters.  
One key issue that emerged was the need to understand the semantics of the data, most notably related to the provenance of data (what type of sensors, their perceived accuracy, and so on). For example, we deployed a range of soil sensors covering a range of prices and qualities. This is important as scientists are interested in what can be achieved from dense deployment of very cheap sensors but equally need more accurate technologies for calibration. Hybrid technologies are therefore important. We have thus devoted significant effort to the resultant (semantic) data model, covering provenance but also other meta-data descriptions and supporting more conceptual reasoning about the data. This data model is briefly mentioned in Sections 4 and 5 and is also the subject of a sister paper which will be published shortly.  
A final important design decision was the provision of data in the cloud as a key means of enabling integration and collaboration but also crucially in supporting open access to data (available through MongoDB). The importance of open data has already been recognised by many observers, most notably by the Royal Society [33]. This is particularly important in the field of environmental and earth sciences as the communities respond to the requirements of a new kind of \"big\" science as required by the challenges of environmental change. Open data also encourages innovation around both uses of the data and (importantly) associated services. Access to the Environmental IoT data is available through the associated data portal, but the open API also encouraged consideration of a range of apps including a \"MySheep\" app and also the potential for integration into the existing MySoils app [34].