The FIESTA-IoT Hackathon took place at the Technical University of Berlin on the 18-19th of March 2018. The event was part of the 2018 IEEE Workshop on Big Data Governance and Metadata and Management (BDGMM). The title of the hackathon was “FIESTA-IoT: EXPERIMENTATION-AS-A-SERVICE FOR BIG IoT TESTBED DATA”. The duration of the hackathon was 24 hours. The hackathon involved the participation of “experimenters” to create novel experiments that involve the discovery of datasets from a range of IoT testbeds that produce data concerning smart cities, smart buildings, environment, maritime, wireless networks, data centres, etc. The hackathon solicited for the design and deployment of advanced (experimental) applications, notably applications that leverage data and resources from multiple administratively and geographically dispersed IoT testbeds. Experimenters were requested to exploit the semantic and interoperable nature of the datasets and data-streams within their experiments.
A total of 8 participants registered for the hackathon. An introduction and tutorial was given at the start. The participants were provided access to data collected during the previous month. Some chose not to continue because they felt the learning curve was too steep for the short period of time, and others did not find the data they wanted to use e.g. marine data. Ultimately two teams of two were formed and continued with the challenge, Team Kyoto and Team Linuxy.
After initial experimentation and understanding of the FIESTA-IoT platform interface, each team decided to focus on a theme. Team Kyoto focused on a range of datasets from the smart city domain, and Team Linuxy focused on datasets from smart buildings. The teams worked hard throughout the day and at their accommodation. The next morning each team presented their solutions.
Team Kyoto presented a solution to support tourism activities of foreign tourists. The goal was to provide detailed information about destinations in advance, which can relate to expectations of accommodation and transport options. A case for using Santander was presented based on the study of tourism statistics for Spain, and the use of MESHSTATS, which provides statistics based on a geographic grid. The dataset used ranged from temperature and illuminance, to parking presence state. The aim was to understand the relationship between quantities measured by PresenceStateParking sensors and actual environment. They also wanted to want to use quantities measured by PresenceStateParking to monitor congestions on streets. The team did identify an issue with the parking presence devices, as the state was changing to frequently.