Abstract

Positioning has been a driving factor in the development of ubiquitous computing applications throughout the past two decades. Numerous devices and techniques have been developed-few of them are actually used commercially. The precision is limited to specific applications, the availability limited to the provider of specific services. Occasionally, few methods have been combined to recalibrate each other by means of data fusion. We present a novel architecture for processing the vast amount of 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 massive redundancy of such nodes and the synergetic heterogeneity of completely different recognition principles allows to tailor the perceived positioning probability to the specific requirements of the target application. A self-learning and self-healing approach to misleading, wrong and outdated pieces of information provides a new quality of interworking position and context aware systems. q 2005 Elsevier B.V. All rights reserved.