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 a specific service.  Occasionally, two methods have been combined
to recalibrate each other.

Most recently, proposals have been made to combine hybrid positioning data from
different technological sources, in order to obtain a higher probability
for a certain position scan by principles of data fusion.

With the penetration of everyday objects with pervasive devices to the
cheapest level, advances in visual tracking and recognition, the arrival of
biometric devices in every office, and wireless sensor networks of a variety
of categories, a new quality of interworking position and context aware
systems becomes available.

The massive redundancy of such nodes and the synergetic heterogeneity of
their recognition principles allows to tailor the perceived positioning
probability to the specific requirements of the target application, and
a self-learning and self-healing approach to misleading, wrong and outdated
pieces of information.