A conflict resolution architecture for the comfort of occupants in intelligent office

Improving energy efficiency in intelligent buildings is an important issue in reducing global warming. Real smart energy efficiency is a combination of energy saving and providing for the satisfaction of occupants. However, the comfort of occupants has an uncertainty in prediction and can conflict with preferences of other occupants. So the "Conflict Resolution Architecture" (CRA) is introduced in this paper to solve the conflict among occupants using the integration of RFID systems and sensor network systems. A CRA consists of two control systems that are applied to two separate types of locations. One is the central rule- based control system in a public zone and the other is the individual utility-based control system in a private zone. Two methods that consist of a priority approach and a privilege-average approach are suggested for the CRA control. Then a CRA is able to make conflict resolution among occupants in an intelligent office.

[1]  Andreas Willig,et al.  Protocols and Architectures for Wireless Sensor Networks , 2005 .

[2]  W. P. Jones,et al.  Air Conditioning Engineering , 1967 .

[3]  Anca D. Galasiu,et al.  Occupant preferences and satisfaction with the luminous environment and control systems in daylit offices: a literature review , 2006 .

[4]  Ajay Kulkarni,et al.  Building Agent-Based Intelligent Workspaces , 2002, International Conference on Internet Computing.

[5]  ZhengChun Mo.  Intelligent buildings and intelligent agents - a human-centered framework for building controls , 2002, Proceedings of the 41st SICE Annual Conference. SICE 2002..

[6]  Hani Hagras,et al.  Embedding Computational Intelligence in Pervasive Spaces , 2007, IEEE Pervasive Computing.

[7]  S. Maithel.  Energy Efficiency and Renewable Energy , 2008 .

[8]  Joshua R. Smith,et al.  RFID-based techniques for human-activity detection , 2005, Commun. ACM.

[9]  Paul Davidsson,et al.  Distributed monitoring and control of office buildings by embedded agents , 2005, Inf. Sci..

[10]  Sajal K. Das,et al.  A cooperative learning framework for mobility-aware resource management in multi-inhabitant smart homes , 2005, The Second Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services.

[11]  Shoji Kurakake,et al.  Ubiquitous sensors based human behavior modeling and recognition using a spatio-temporal representation of user states , 2004, 18th International Conference on Advanced Information Networking and Applications, 2004. AINA 2004..

[12]  Alice M. Agogino,et al.  Towards embedded wireless-networked intelligent daylighting systems for commercial buildings , 2006, IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC'06).

[13]  Kecheng Liu,et al.  Co-ordinated Management of Intelligent Pervasive Spaces , 2007, 2007 5th IEEE International Conference on Industrial Informatics.

[14]  Young-Koo Lee,et al.  Conflict Resolution and Preference Learning in Ubiquitous Environment , 2006, ICIC.

[15]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[16]  Kecheng Liu,et al.  A Multi-Agent System for Building Control , 2006, 2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology.