Smart cities are becoming a vibrant application domain for a number of science fields. As such, service providers and stakeholders are beginning to integrate co-creation aspects into current implementations to shape the future smart city solutions. In this context, holistic solutions are required to test such aspects in real city-scale IoT deployments, considering the complex city ecosystems. In this work, we discuss OrganiCity's implementation of an Experimentation-as-a-Service framework, presenting a toolset that allows developing, deploying and evaluating smart city solutions in a one-stop shop manner. This is the first time such an integrated toolset is offered in the context of a large-scale IoT infrastructure, which spans across multiple European cities. We discuss the design and implementation of the toolset, presenting our view on what Experimentationas-a-Service should provide, and how it is implemented. We present initial feedback from 25 experimenter teams that have utilized this toolset in the OrganiCity project, along with a discussion on two detailed actual use cases to validate our approach. Learnings from all experiments are discussed as well as architectural considerations for platform scaling. Our feedback from experimenters indicates that Experimentation-as-a-Service is a viable and useful approach.

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The Semantic Web (or Web of Data) represents the successful efforts towards linking and sharing data over the Web. The cornerstones of the Web of Data are RDF as data format and SPARQL as de-facto standard query language. Recent trends show the evolution of the Web of Data towards the Web of Things, integrating embedded devices and smart objects. Data stemming from such devices do not share a common format, making the integration and querying impossible. To overcome this problem, we present our approach to make embedded systems first-class citizens of the Web of Things. Our framework abstracts from individual deployments to represent them as common data sources in line with the ideas behind the Semantic Web. This includes the execution of arbitrary SPARQL queries over the data from a pool of embedded devices and/or external data sources. Handling verbose RDF data and executing SPARQL queries in an embedded network poses major chal-lenges to minimize the involved processing and communication cost. We therefore present an in-network query processor aiming to push processing steps onto devices. We demonstrate the practical application and the potential benefits of our framework in a comprehensive evaluation using a real-world deployment and a range of SPARQL queries stemming from a common use case of the Web of Things.

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