Consistency Analysis of Sensor Data Distribution

TL;DR

This paper investigates the probability of consistency in sensor data distribution systems and evaluates models to accurately represent their dynamics, considering various parameters affecting system reliability.

Contribution

It introduces a framework for analyzing SDDS consistency and compares Markovian and semi-Markov models for better accuracy in different operational conditions.

Findings

Markovian models may not fit all SDDS scenarios

Erlang phase-type approximations improve modeling accuracy

Semi-Markov models can account for overlapping events

Abstract

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.