Application of Fuzzy Logic for Improving Human Sleeping Conditions in an Ambient Intelligence Testbed

Ambient Intelligence (AmI) deals with a new world of ubiquitous computing devices, where physical environments interact intelligently and unobtrusively with people. AmI environments can be diverse, such as homes, offices, meeting rooms, schools, hospitals, control centers, vehicles, tourist attractions, stores, sports facilities, and music devices. In our previous work, we presented the implementation and evaluation of actor node for AmI testbed. In this paper, we introduce the implementation of the AmI testbed. We present the simulation results of the proposed Fuzzy-based Sleeping Condition System (FSCS) considering four parameters: room lighting, humidity, temperature and noise. The simulation results show that different parameters have different effects on human sleeping condition.

[1]  Hans-Jürgen Zimmermann,et al.  Fuzzy Set Theory - and Its Applications , 1985 .

[2]  Toshinori Munakata,et al.  Fuzzy systems: an overview , 1994, CACM.

[3]  F. Martin McNeill,et al.  Fuzzy Logic: A Practical Approach , 1994 .

[4]  Witold Pedrycz,et al.  Ambient Intelligence, Wireless Networking, And Ubiquitous Computing , 2006 .

[5]  Ebrahim H. Mamdani,et al.  A linguistic self-organizing process controller , 1979, Autom..

[6]  Ana M. Bernardos,et al.  CASanDRA: A Framework to Provide Context Acquisition Services ANd Reasoning Algorithms for Ambient Intelligence Applications , 2009, 2009 International Conference on Parallel and Distributed Computing, Applications and Technologies.

[7]  Faiyaz Doctor,et al.  A hardware/software embedded agent for real-time control of ambient-intelligence environments , 2012, 2012 IEEE International Conference on Fuzzy Systems.

[8]  Athanasios V. Vasilakos,et al.  A Survey on Ambient Intelligence in Healthcare , 2013, Proceedings of the IEEE.

[9]  Boris E. R. de Ruyter,et al.  New research perspectives on Ambient Intelligence , 2009, J. Ambient Intell. Smart Environ..

[10]  Radu Marculescu,et al.  Ambient intelligence visions and achievements: linking abstract ideas to real-world concepts , 2003, 2003 Design, Automation and Test in Europe Conference and Exhibition.

[11]  L. Zadeh,et al.  Fuzzy Logic for the Management of Uncertainty , 1992 .

[12]  George J. Klir,et al.  Fuzzy sets, uncertainty and information , 1988 .

[13]  Lothar Litz,et al.  NCS testbed for ambient intelligence , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[14]  Abraham Kandel,et al.  Fuzzy Expert Systems , 1991 .

[15]  Fariba Sadri,et al.  Ambient intelligence: A survey , 2011, CSUR.