In this paper we analyze the probability of consistency of sensor datadistribution systems (SDDS), and determine suitable evaluation models. Thisproblem is typically difficult, since a reliable model taking into account allparameters and processes which affect the system consistency is unavoidablyvery complex. The simplest candidate approach consists of modeling the statesojourn time, or holding time, as memoryless, and resorting to the well knownsolutions of Markovian processes. Nevertheless, it may happen that thisapproach does not fit with some working conditions. In particular, the correctmodeling of the SDDS dynamics requires the introduction of a number ofparameters, such as the packet transfer time or the packet loss probability,the value of which may determine the suitability of unsuitability of theMarkovian model. Candidate alternative solutions include the Erlang phase-typeapproximation of nearly constant state holding time and a more refined model toaccount for overlapping events in semi-Markov processes.
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