  Sensor-driven systems are increasingly ubiquitous: they provide both data and
information that can facilitate real-time decision-making and autonomous
actuation, as well as enabling informed policy choices by service providers and
regulators. But can we guarantee these system do what we expect, can their
stake-holders ask deep questions and be confident of obtaining reliable
answers? This is more than standard software engineering: uncertainty pervades
not only sensors themselves, but the physical and digital environments in which
these systems operate. While we cannot engineer this uncertainty away, through
the use of models we can manage its impact in the design, development and
deployment of sensor network software. Our contribution consists of two new
concepts that improve the modelling process: frames of reference bringing
together the different perspectives being modelled and their context; and the
roles of different types of model in sensor-driven systems. In this position
paper we develop these new concepts, illustrate their application to two
example systems, and describe some of the new research challenges involved in
modelling for assurance.
