A sustainable open data platform for air quality data

Smart Cities need (sensor) data for better decision-making. However, while there are vast amounts of data available about and from cities, an intermediary is needed that connects and interprets sensor data on Web-scale. Today, governments are struggling to publish open data in a sustainable, predictable and cost-effective way. Our research question considers what methods for publishing and archiving linked open data time series, in specific air quality data, are suitable in a sustainable and costeffective way. Based on a scenario, co-created with various Flemish governmental stakeholders, we benchmarked two Internet of Things reference architectures (FiWare Quantum Leap API and Linked Time Series Server) and calculated the cost for both data publisher and consumer. Results show that applying Linked Times Series on public endpoints for air quality data will lower the cost of publishing and will raise availability because of a better Web caching strategy.