The Internet of Things (IoT) concept has attracted a lot of attention from the research and innovation community for a number of years already. One of the key drivers for this hype towards the IoT is its applicability to a plethora of different application domains. However, infrastructures enabling experimental assessment of IoT solutions are scarce. Being able to test and assess the behavior and the performance of any piece of technology (i.e., protocol, algorithm, application, service, etc.) under real-world circumstances is of utmost importance to increase the acceptance and reduce the time to market of these innovative developments. This paper describes the federation of eleven IoT deployments from heterogeneous application domains (e.g., smart cities, maritime, smart building, crowd-sensing, smart grid, etc.) with over 10,000 IoT devices overall which produce hundreds of thousands of observations per day. The paper summarizes the resources that are made available through a cloud-based platform. The main contributions from this paper are twofold. In the one hand, the insightful summary of the federated data resources are relevant to the experimenters that might be seeking for an experimental infrastructure to assess their innovations. On the other hand, the identification of the challenges met during the testbed integration process, as well as the mitigation strategies that have been implemented to face them, are of interest for testbed providers that can be considering to join the federation.

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Having 6500 integrated sensors and a wide network of embedded systems, the ADREAM building can be categorized as one of the prototype smart buildings of France. As needs for intelligent management of energy are growing, the associated project provides a multidisciplinary platform of experimentation, developing solutions for efficient Energy Networks, HVAC Systems, Photovoltaics, and Smart Grids. This paper provides the overview of the project along with three different modeling techniques, illustrating their strengths and limitations on simulating the thermal behavior of the building and the functioning of the different energy systems. A thermal model of the building was developed and calibrated for energy consumption analysis and prediction, using the software Pleiades+Comfie. A "black box" model was developed using artificial neural networks for the simulation of energy system parameters and the exploration of efficient control strategies. The last method provides the overview of developing an allinclusive electrically equivalent physical model with Matlab/Simulink for simulating the global functioning of the building and its HVAC systems. The conclusion addresses the utility of exploring and combining different types of models for optimizing the energy management of Smart Buildings.