Towards an IoT big data analytics framework: Smart buildings systems

Publication Type:
Conference Proceeding
Proceedings - 18th IEEE International Conference on High Performance Computing and Communications, 14th IEEE International Conference on Smart City and 2nd IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2016, 2017, pp. 1325 - 1332
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© 2016 IEEE. There is a growing interest in IoT-enabled smart buildings. However, the storage and analysis of large amount of high-speed real-time smart building data is a challenging task. There are a number of contemporary Big Data management technologies and advanced analytics techniques that can be used to deal with this challenge. There is a need for an integrated IoT Big Data Analytics (IBDA) framework to fill the research gap in the Big Data Analytics domain. This paper presents one such IBDA framework for the storage and analysis of real time data generated from IoT sensors deployed inside the smart building. The initial version of the IBDA framework has been developed by using Python and the Big Data Cloudera platform. The applicability of the framework is demonstrated with the help of a scenario involving the analysis of real-time smart building data for automatically managing the oxygen level, luminosity and smoke/hazardous gases in different parts of the smart building. The initial results indicate that the proposed framework is fit for the purpose and seems useful for IoT-enabled Big Data Analytics for smart buildings. The key contribution of this paper is the complex integration of Big Data Analytics and IoT for addressing the large volume and velocity challenge of real-time data in the smart building domain. This framework will be further evaluated and extended through its implementation in other domains.
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