  In this paper we analyze the probability of consistency of sensor data
distribution systems (SDDS), and determine suitable evaluation models. This
problem is typically difficult, since a reliable model taking into account all
parameters and processes which affect the system consistency is unavoidably
very complex. The simplest candidate approach consists of modeling the state
sojourn time, or holding time, as memoryless, and resorting to the well known
solutions of Markovian processes. Nevertheless, it may happen that this
approach does not fit with some working conditions. In particular, the correct
modeling of the SDDS dynamics requires the introduction of a number of
parameters, such as the packet transfer time or the packet loss probability,
the value of which may determine the suitability of unsuitability of the
Markovian model. Candidate alternative solutions include the Erlang phase-type
approximation of nearly constant state holding time and a more refined model to
account for overlapping events in semi-Markov processes.
