Abstract

Positioning, as a key component of context, has been a driving factor in the development of ubiquitous computing applications throughout the past two decades. However, the precision is limited to specific applications, the availability limited to the provider of specific services. Combining multiple methods allows to recalibrate each other by means of data fusion. A novel architecture processes the data from pervasive devices penetrating everyday objects to the cheapest level. Location information can be inferred from infrastructure deployed for different purposes, only partly designed for positioning in the first place. The redundant heterogeneity of completely different recognition principles allows to tailor the perceived positioning probability to the specific requirements of the target application.