For smart decision making, user agents need live and historic access to open data from sensors installed in the public domain. In contrast to a closed environment, for Open Data and federated query processing algorithms, the data publisher cannot anticipate in advance on specific questions, nor can it deal with a bad costefficiency of the server interface when data consumers increase. When publishing observations from sensors, different fragmentation strategies can be thought of depending on

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... the historic data needs to be queried. Furthermore, both publish/subscribe and polling strategies exist to publish live updates. Each of these strategies come with their own trade-offs regarding cost-efficiency of the server-interface, user-perceived performance and cpu use. A polling strategy where multiple observations are published in a paged collection was tested in a proof of concept for parking spaces availability. In order to understand the different resource trade-offs presented by publish/subscribe and polling publication strategies, we devised an experiment on two machines, for a scalability test. The preliminary results were inconclusive and suggest more large scale tests are needed in order to see a trend. While the large-scale tests will be performed in future work, the proof of concept helped to identify the technical Open Data principles for the 13 biggest cities in Flanders.