Development of IoT Telemetry for Local Control System and Management Software
Nika, Kolindo (2023)
2023
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2023120534482
https://urn.fi/URN:NBN:fi:amk-2023120534482
Tiivistelmä
This thesis was a project that included the design, development, and implementation of a telemetry system within local control system and management software, leveraging state-of-the-art Internet of Things (IoT) and data engineering technologies. The primary objective of this research was to navigate through the complexities of telemetry data collection, modeling, storage, analysis, and their integration within the software environment.
Central to this thesis was the development of a comprehensive system architecture. This process involved the identification and selection of key telemetry metrics from the software and the development of a multi-layered SQL database architecture hosted on AWS Aurora. The implementation aspect included the construction of a telemetry module using the JUCE framework and C++, which logs data locally in JSON format and periodically transmits it to an API gateway. Additionally, a Python-based data processing application was developed and integrated with AWS EKS, ensuring efficient and seamless data ingestion, transformation, and storage. An essential component of the project was the development of an advanced Excel dashboard, enhanced by VBA capabilities, for comprehensive telemetry data analysis. This analysis dashboard not only provided valuable insights into system performance but also identified areas for enhancement. A significant focus was placed on maintaining the security and integrity of the telemetry data throughout the process.
As a result, a comprehensive telemetry system was developed and implemented within local control system and management software. Throughout the development process, emphasis was placed on addressing various case scenarios and efficiently collecting, processing, and storing telemetry data while maintaining data integrity. The implementation of the dashboard demonstrated the system's capability to convert complex telemetry data into actionable insights. In conclusion, this system was strategically designed and deployed with the aim to improve service quality and enable proactive maintenance strategies.
Central to this thesis was the development of a comprehensive system architecture. This process involved the identification and selection of key telemetry metrics from the software and the development of a multi-layered SQL database architecture hosted on AWS Aurora. The implementation aspect included the construction of a telemetry module using the JUCE framework and C++, which logs data locally in JSON format and periodically transmits it to an API gateway. Additionally, a Python-based data processing application was developed and integrated with AWS EKS, ensuring efficient and seamless data ingestion, transformation, and storage. An essential component of the project was the development of an advanced Excel dashboard, enhanced by VBA capabilities, for comprehensive telemetry data analysis. This analysis dashboard not only provided valuable insights into system performance but also identified areas for enhancement. A significant focus was placed on maintaining the security and integrity of the telemetry data throughout the process.
As a result, a comprehensive telemetry system was developed and implemented within local control system and management software. Throughout the development process, emphasis was placed on addressing various case scenarios and efficiently collecting, processing, and storing telemetry data while maintaining data integrity. The implementation of the dashboard demonstrated the system's capability to convert complex telemetry data into actionable insights. In conclusion, this system was strategically designed and deployed with the aim to improve service quality and enable proactive maintenance strategies.