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Title:
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CLOUD COMPUTING AND VALIDATED LEARNING FOR ACCELERATING INNOVATION IN IOT |
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Author(s):
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George Suciu1, Gyorgy Todoran, Alexandru Vulpe, Victor Suciu, Cristina Butca, Romulus Cheveresan |
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ISBN:
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978-989-8533-40-1 |
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Editors:
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Miguel Baptista Nunes and Maggie McPherson |
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Year:
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2015 |
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Edition:
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Single |
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Keywords:
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Cloud computing; telemetry, M2M, validated learning, IoT. |
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Type:
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Short Paper |
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First Page:
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178 |
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Last Page:
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182 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Innovation in Internet of Things (IoT) requires more than just creation of technology and use of cloud computing or big data platforms. It requires accelerated commercialization or aptly called go-to-market processes. To successfully accelerate, companies need a new type of product development, the so-called validated learning process. Furthermore, the development of the IoT paradigm has advanced the research on Machine to Machine (M2M) communications and has enabled novel acceleration platforms. However, there is a need for converging currently decentralized cloud systems, general software for processing big data and IoT systems. The paper describes a cloud platform that will bring together several services and instruments that the companies usually rely on in their go-to-market process. The main contribution of this paper consists in the integration of several services in the platform. The proposed platform will allow companies to bring their products to market faster and in a more successful manner, will be accessed via the Internet and will host the tools and virtual environments needed for the go-to-market accelerated process. Finally, is presented the acceleration results of a Cloud IoT architecture for processing big data from M2M telemetry. |
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