Wu, S. and Clements-Croome, D. (2007) Understanding the Indoor Environment through Mining Sensory Data - A Case Study. Energy and Buildings, 39 (11). pp. 1183-1191. ISSN 0378-7788.
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A wireless sensor network (WSN) is a group of sensors linked by wireless medium to perform distributed sensing tasks. WSNs have attracted a wide interest from academia and industry alike due to their diversity of applications, including home automation, smart environment, and emergency services, in various buildings. The primary goal of a WSN is to collect data sensed by sensors. These data are characteristic of being heavily noisy, exhibiting temporal and spatial correlation. In order to extract useful information from such data, as this paper will demonstrate, people need to utilise various techniques to analyse the data. Data mining is a process in which a wide spectrum of data analysis methods is used. It is applied in the paper to analyse data collected from WSNs monitoring an indoor environment in a building. A case study is given to demonstrate how data mining can be used to optimise the use of the office space in a building.
|Additional information:||Unmapped bibliographic data: PY - 2007/// [EPrints field already has value set] AD - Centre for Resource Management and Efficiency, Sustainable Systems Department, Cranfield University, Bedfordshire MK43 0AL, United Kingdom [Field not mapped to EPrints] AD - School of Construction Management and Engineering, The University of Reading, RG6 6AW Reading, United Kingdom [Field not mapped to EPrints] JA - Energy Build. [Field not mapped to EPrints]|
|Uncontrolled keywords:||Clustering, Data mining, Indoor environment, Wireless sensor network, Automation, Clustering algorithms, Data mining, Data reduction, Industrial applications, Office buildings, Emergency services, Indoor environment, Office space, Wireless sensor networks|
|Subjects:||H Social Sciences
H Social Sciences > HA Statistics > HA33 Management Science
|Divisions:||Faculties > Social Sciences > Kent Business School > Management Science|
|Depositing User:||Cathy Norman|
|Date Deposited:||01 Oct 2012 15:43|
|Last Modified:||09 Jan 2013 10:45|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/31018 (The current URI for this page, for reference purposes)|
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