Publication Details

This conference paper was originally published as Desmet, A, Naghdy, F & Ros, M, Embedding Distributed Learning Algorithms in Wireless Ad-Hoc Control Networks, International Conference on Intelligent and Advances Systems 2007, Kuala Lumpur, Malaysia, 25 - 28 November 2007. Copyright Institute of Electrical and Electronics Engineers IEEE 2007. Original conference paper available here


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 algorithm’s response to different rule selection criterions.



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