Skip to main content
placeholder image

Embedding distributed learning algorithms in wireless ad-hoc control networks

Conference Paper


Download full-text (Open Access)

Abstract


  • With the advances in soft computing techniques

    and agent technologies, the concept of home ambient

    intelligence is becoming more and more realistic. Living in a

    building that adapts itself to the users and assists them in

    reducing their energy consumption is now within reach. The

    main technical barrier comes from hardware: servers and

    industrial control networks do not fit in a house. With the

    availability of dedicated wireless solutions and low-cost small

    computation units, the platform to implement task

    distribution in a control network is now feasible and cost

    efficient. This paper explores the possibilities of fitting a

    distributed learning algorithm for home ambient intelligence

    in a wireless network of sensors and actuators, driven by very

    limited microcontrollers. The chosen hardware platform is the

    WACNet: Wireless Ad-hoc Control Network.

    The concept of WACNet is introduced and the test-bed

    developed for its study is explained. The fuzzy learning

    algorithm is then introduced and its implementation is studied.

    The results of a test are provided and some conclusions are

    drawn, mainly focusing on accuracy and the algorithms

    response to different rule selection criterions.

Publication Date


  • 2007

Citation


  • Desmet, A., Naghdy, F. & Ros, M. B. 2007, 'Embedding distributed learning algorithms in wireless ad-hoc control networks', International Conference on Intelligent and Advanced Systems, IEEE, IEEE Explore Online: ieeexplore.ieee.org, pp. 338-342.

Scopus Eid


  • 2-s2.0-57949093009

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1708&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/703

Has Global Citation Frequency


Start Page


  • 338

End Page


  • 342

Place Of Publication


  • IEEE Explore Online: ieeexplore.ieee.org

Abstract


  • With the advances in soft computing techniques

    and agent technologies, the concept of home ambient

    intelligence is becoming more and more realistic. Living in a

    building that adapts itself to the users and assists them in

    reducing their energy consumption is now within reach. The

    main technical barrier comes from hardware: servers and

    industrial control networks do not fit in a house. With the

    availability of dedicated wireless solutions and low-cost small

    computation units, the platform to implement task

    distribution in a control network is now feasible and cost

    efficient. This paper explores the possibilities of fitting a

    distributed learning algorithm for home ambient intelligence

    in a wireless network of sensors and actuators, driven by very

    limited microcontrollers. The chosen hardware platform is the

    WACNet: Wireless Ad-hoc Control Network.

    The concept of WACNet is introduced and the test-bed

    developed for its study is explained. The fuzzy learning

    algorithm is then introduced and its implementation is studied.

    The results of a test are provided and some conclusions are

    drawn, mainly focusing on accuracy and the algorithms

    response to different rule selection criterions.

Publication Date


  • 2007

Citation


  • Desmet, A., Naghdy, F. & Ros, M. B. 2007, 'Embedding distributed learning algorithms in wireless ad-hoc control networks', International Conference on Intelligent and Advanced Systems, IEEE, IEEE Explore Online: ieeexplore.ieee.org, pp. 338-342.

Scopus Eid


  • 2-s2.0-57949093009

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1708&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/703

Has Global Citation Frequency


Start Page


  • 338

End Page


  • 342

Place Of Publication


  • IEEE Explore Online: ieeexplore.ieee.org