Design and development of a AWS-based dynamic application server as part of a LoRaWAN system used for IoT data gathering of microclimate data. The server is composed of a high performance AWS DB and two scalable Node.js servers that runs on AWS computing capacities and present an intuitive user interface for viewing and downloading gathered data.
Global warming is one of the hardest and most urgent problems in the world today. In order to offer new solutions and ideas on slowing down the process and its repercussions, there is a need for an expansion of the study on that matter. But this expansion could not be achieved without an advanced gathering of data, both globally and locally, by gathering regional microclimate data.
LoRaWAN systems, which can communicate efficiently with remote IoT devices, may become useful when developing those required data gathering systems. By building such systems, a detailed microclimate picture could be built by gathering various weather measures. This picture could help in understanding the environment and the phenomena occurring in it. For this purpose, A stable LoRaWAN system depends on a persistent application to store incoming data, and through which the data could be retrieved for further study and analysis.
By right utilization of commercial cloud computing services, a smart end-user application can be developed, one that has the required properties to work efficiently with a wide and fully-loaded LoRaWAN system: A durable, stable and scalable application, that guarantees that the entire system can handle large amounts of data and user requests. By designing such a system, the application can ensure that the data gathering will work properly even on a big scale system, and the provision of data to research institutes will become more reliable.
During this project, a dynamic cloud-based end-user application for a LoRaWAN system was designed and developed. This application is based on the AWS cloud computing services both for data storage and for the run of two application servers and offers a simple and easy-to-use user interface for viewing data and download options for the researcher’s private PC for further analysis.
