Understanding the indoor environment through mining sensory data—A case study

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.

[1]  Kamin Whitehouse,et al.  Achieving stable network performance for wireless sensor networks , 2008, SenSys '08.

[2]  Gianluca Bontempi,et al.  An adaptive modular approach to the mining of sensor network data , 2005 .

[3]  Derek Clements-Croome.  Creating the Productive Workplace , 1999 .

[4]  Andreas Krause,et al.  Intelligent light control using sensor networks , 2005, SenSys '05.

[5]  J. H. Ward.  Hierarchical Grouping to Optimize an Objective Function , 1963 .

[6]  Vishal Garg,et al.  Smart occupancy sensors to reduce energy consumption , 2000 .

[7]  C. Ratti,et al.  Energy consumption and urban texture , 2005 .

[8]  Michael C. Mozer,et al.  The Neural Network House: An Environment that Adapts to its Inhabitants , 1998 .

[9]  William M. Healy.  Lessons Learned in Wireless Monitoring , 2005 .

[10]  Dimitrios Makrakis,et al.  Sensor-based information appliances , 2000 .

[11]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery in Databases , 1996, AI Mag..

[12]  P. Nafstad,et al.  Occupancy density and benefits of demand-controlled ventilation in Norwegian primary schools , 2005 .

[13]  D. Kolokotsaa,et al.  Genetic algorithms optimized fuzzy controller for the indoor environmental management in buildings implemented using PLC and local operating networks , 2003 .

[14]  A. Agogino,et al.  Wireless Sensor Networks for Commercial Lighting Control : Decision Making with Multi-agent Systems , 2004 .

[15]  Charles E. Perkins,et al.  Ad Hoc Networking , 2001 .

[16]  Alice M. Agogino,et al.  Towards Demand-Responsive Intelligent Lighting With Wireless Sensing and Actuation , 2004 .

[17]  S. Manesis,et al.  A Survey of Applications of Wireless Sensors and Wireless Sensor Networks , 2005, Proceedings of the 2005 IEEE International Symposium on, Mediterrean Conference on Control and Automation Intelligent Control, 2005..

[18]  B. R. Badrinath,et al.  Cleaning and querying noisy sensors , 2003, WSNA '03.

[19]  Danco Davcev,et al.  Tracking of unusual events in wireless sensor networks based on artificial neural-networks algorithms , 2005, International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II.

[20]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[21]  William Frawley,et al.  Knowledge Discovery in Databases , 1991 .

[22]  L. Yann-Ael,et al.  Round Robin Cycle for Predictions in Wireless Sensor Networks , 2005, 2005 International Conference on Intelligent Sensors, Sensor Networks and Information Processing.