# The Design and Deployment of an End-To-End IoT Infrastructure for the Natural Environment
## 6 Discussion
### Technological Contributions  
This project was by definition an \"in the wild\" deployment in a complex and remote area of North Wales, and this dictated a lot of the design choices. In addition, the work was in support of real scientific enquiry and hence had to provide a complete and comprehensive solution from a sensor to data portal. This resulted in a pragmatic and end-to-end architecture that achieves seamless integration across both Internet of Things and cloud domains. The pragmatism is most evident in the network architecture (as presented in Section 4). For example, we initially planned to deploy a mesh network architecture for added coverage and resilience. With the range and effectiveness of the XBee Pro radios, this was however deemed unnecessary, and instead, we opted for the simpler point to point architecture, supplemented by the additional bridging nodes with long range radios (effectively better antennae). With careful placement of the bridging node(s), this solution proved successful. We also initially planned to achieve end-to-end integration through IP, as advocated by many researchers in the IoT community [29; 30]. This however was not implemented, and instead, we opted for a more \"systems-of-systems\" architecture, reasoning about individual systems and the boundaries between them. This again proved to be a successful strategy and allowed us to optimise the protocols in the IoT deployment site for their particular purpose. The micro-server was then particularly important in acting as an Internet gateway in our systems-of-systems approach. Further investigation of systems-of-systems architectures is being carried out in the Dionasys project ([[https://www.dionasys.eu./](https://www.dionasys.eu./)]([https://www.dionasys.eu./](https://www.dionasys.eu./))); and one of their publications is [31]. The work highlighted the importance of a flexible and adaptive placement of computation in the resultant systems-of-systems approach. Our experiences indicate that it is far too simplistic to bring the data back to the cloud for processing, and this is particularly important in this domain. For example, in many circumstances, the data is rather static and predictable, but at times of extreme events, such as storms/heavy rainfall, the data then becomes critical to understand flooding behaviours of potential water quality issues. It is also too draining in resources (bandwidth and battery) to communicate all data and also sensors such as our accelerometers (on the sheep) generated vast volumes of data. These insights have led to future work in (i) the dynamic enhancement of the micro-server to be an edge device looking at constructing a micro-cloud out of a number of Raspberry Pis, (ii) strategies looking at the dynamic placement of computation on the resultant complex distributed environment (including the pushing of behaviour to end or edge devices, and (iii) self-organisation strategies for the above based on machine learning. The combination of IoT technology and cloud computing is powerful--with IoT providing a source of rich, ubiquitous data coupled with an ability to intervene in the real world through means of actuation and cloud computing offering massive and elastic capacity in terms of storage and processing, coupled with an associated service architecture to interpret, manipulate, and visualise this data. There is however one missing piece--data science--and it is clear from our work that more effort is required to make sense of the rich volumes of data generated by an Environmental IoT. It is also clear that this domain represents different challenges for data science than many areas of big data; the data in this area tends to be a larger collection of heterogeneous data elements requiring complex analysis around, for example, interdependency. Researchers refer to this as the \"long tail of science\" [32].