Today, the Internet of Things (IoT) is comprised of vertically oriented platforms for things. Developers who want to use them need to negotiate access individually and need to adapt to the platform-specific API and information models. Having to do these efforts for each platform often outweighs the possible gains for application developers to adapt their applications to multiple platforms. This fragmentation of the IoT and the missing interoperability result in high entry barriers for developers and currently prevent the emergence of broadly accepted IoT ecosystems. This article presents the work of the BIG IoT project that aims at igniting an IoT ecosystem as part of the European Platform Initiative (IoT EPI). We introduce an architectural model for IoT ecosystems, and highlight five common interoperability patterns that need to be supported for enabling cross-platform interoperability and establishing successful IoT ecosystems.

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This paper presents a methodology developed for a study to evaluate the state of the art of automated map generalization in commercial software without applying any customization. The objectives of this study are to learn more about generic and specific requirements for automated map generalization, to show possibilities and limitations of commercial generalization software, and to identify areas for further research. The methodology had to consider all types of heterogeneity to guarantee independent testing and evaluation of available generalization solutions. The paper presents the two main steps of the methodology. The first step is the analysis of map requirements for automated generalization, which consisted of sourcing representative test cases, defining map specifications in generalization constraints, harmonizing constraints across the test cases, and analyzing the types of constraints that were defined. The second step of the methodology is the evaluation of generalized outputs. In this step, three evaluation methods were integrated to balance between human and machine evaluation and to expose possible inconsistencies. In the discussion the applied methodology is evaluated and areas for further research are identified.

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