Abstract
As the scale and heterogeneity of experimental environments increases, the selection of adequate testbed resources becomes a daunting task for the experimenter. Wrong choices or unexpected resource behavior can significantly decrease an experimenter’s productivity. These challenges are further amplified by the recent trend of moving testbeds from isolated labs to unpredictable real world environments to favor experimental evaluation under realistic conditions. This paper presents a framework for resource selection in large scale and heterogeneous Internet of Things testbeds, in order to support the experimenter with an increased understanding of available testbed resources, their expected behavior and topological relationships in the experimentation environment. Through an evaluation case study we demonstrate the effectiveness of our proposed framework