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The digital universe is growing at significant rates in recent years. One of the main responsible for this sentence is the Internet of Things (IoT), which requires middlewares capable of handling this increase of data volume in real-time. Solutions modeled at software, hardware and/or architecture level present limitations to handle such load, facing a scalability problem in the IoT scope. In this context, this article presents a model named Eliot (Elasticity-driven Internet of Things) which combines both cloud and high performance computing to address the IoT scalability problem in EPCglobal-compliant architectures. Based on the Eliot model, we developed a prototype that can run as a plugin solution together with any current EPCglobal-compliant middleware. The results are encouraging, presenting significant performance gains when comparing both elastic and non-elastic executions.

To Eduardo R., to Fernanda, to Lucas, to Rodolfo, to Vinicius; To Ana Paula, to Eric, to Izabela Q., to Mônica; To Priscilla, to Ronas, to Tauãnara, to William, to Willian;A heartfelt thanks.Ticking away the moments that make up a dull day; You fritter and waste the hours in an offhand way.

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Abstract:The convergence of Internet of "things" (IoT) with big data platforms and cloud computing is already happening. However, the vast majority, if not all the proposals are based on the current Internet technologies. The convergence of IoT, big data and cloud in "clean slate" architectures is an unexplored topic. In this article, we discuss this convergence considering the viewpoint of a "clean slate" proposal called NovaGenesis. We specify a set of NovaGenesis services to publish sensor device's data in distributed hash tables employing selfverifying addresses and contract-based trust network formation. IoT devices capabilities and configurations are exposed to software-controllers, which control their operational parameters. The specification covers how the "things" sensed information are subscribed by a big data service and injected in Spark big data platform, allowing NovaGenesis services to subscribe data analytics from Spark. Future work include implementation of the proposed specifications and further investigation of NovaGenesis services performance and scalability.