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The Internet of Things (IoT) is transforming the surrounding everyday physical objects into an ecosystem of information that enriches our everyday life. The IoT represents the convergence of advances in miniaturization, wireless connectivity, and increased data storage and is driven by various sensors. Sensors detect and measure changes in position, temperature, light, and many others; furthermore, they are necessary to turn billions of objects into data-generating “things” that can report on their status and often interact with their environment. Application and service development methods and frameworks are required to support the realization of solutions covering data collection, transmission, data processing, analysis, reporting, and advanced querying. This paper introduces the SensorHUB framework that utilizes the state-of-the-art open source technologies and provides a unified tool chain for IoT related application and service development. SensorHUB is both a method and an environment to support IoT related application and service development; furthermore, it supports the data monetization approach, that is, provides a method to define data views on top of different data sources and analyzed data. The framework is available in a Platform as a Service (PaaS) model and has been applied for the vehicle, health, production lines, and smart city domains.

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This paper describes the implementation of network coding on OpenGL-enabled graphics cards. Network coding is an interesting approach to increase the capacity and robustness in multi-hop networks. The current problem is to implement random linear network coding on mobile devices which are limited in computational power, energy, and memory. Some mobile devices are equipped with a 3D graphics accelerator, which could be used to do most of the RLNC related calculations. Such a cross-over have already been used in com putationally demanding research tasks as in physics or medicine. As a first step the paper focuses on the implementation of RLNC using the OpenGL library and NVidia's Cg toolkit on desktop PCs and laptops. Several measurement results show that the implementation on the graphics accelerator is outperforming the CPU by a significant margin. The OpenGL implementation performs relatively better with larger generation sizes due to the parallel nature of GPUs. Therefore the paper shows an appealing solution for the future to perform network coding on mobile devices.