{"title":"Embedding distributed learning algorithms in Wireless Ad-Hoc Control Networks","authors":"A. Desmet, F. Naghdy, M. Ros","doi":"10.1109/ICIAS.2007.4658403","DOIUrl":null,"url":null,"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 algorithmpsilas response to different rule selection criterions.","PeriodicalId":228083,"journal":{"name":"2007 International Conference on Intelligent and Advanced Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 International Conference on Intelligent and Advanced Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIAS.2007.4658403","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
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 algorithmpsilas response to different rule selection criterions.