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Cloud supported building data analytics

Petri, Ioan, Rana, Omer, Rezgui, Yacine, Li, Haijiang, Beach, Thomas, Zou, Mengsong, Diaz-Montes, Javier and Parashar, Manish 2014. Cloud supported building data analytics. Presented at: 2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), Chicago, IL, USA, 26-29 May 2014. Cluster, Cloud and Grid Computing (CCGrid), 2014 14th IEEE/ACM International Symposium on. IEEE, pp. 641-650.

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Abstract

With increasing availability of instrumented infrastructures in built environments, it is necessary to understand how such data will be stored, processed and analysed in a timely manner. Many "smart cities" applications, for instance, identify how data from building sensors can be combined together to support applications such as emergency response, energy management, etc. Enabling sensor data to be transmitted to a Cloud environment for processing provides a number of benefits, such as scalability and elastic provisioning of computational resources - as the total data size may not be known apriori. In this application-based case study, we describe the integration of an in-building sensor network (both for sensing and actuation) with a distributed Cloud environment. Energy optimisation in buildings represents a class of problems that requires significant computational resources and generally is a time consuming process. We describe the use of Cloud computing for efficiently running and deploying Energy Plus simulations with sensor data in order to fulfil a number of energy related objectives for buildings. We describe and evaluate the establishment of such a sensor based application using a Comet Cloud implementation with data collection from a real building pilot. Although our focus is on a single application, the general architecture and analysis carried out can be generalised to other similar scenarios.

Item Type: Conference or Workshop Item (Paper)
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher: IEEE
Last Modified: 30 Aug 2017 06:26
URI: http://orca-mwe.cf.ac.uk/id/eprint/92583

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