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This paper describes the features of a data acquisition system modeling based on queueing theory method. The main elements of the studied data acquisition system structure are sample hold amplifiers and dual-slope analogue digital converters. In case of the threshold control of technological facilities parameters, alarm signals in the system input are presented as a flow of customers with the Poisson intensity distribution. A converting time of these alarms in analogue digital converters depends on signal levels and is described by an exponential distribution. In this case we present the data acquisition system as the Markov model of the multichannel queueing system with a limited queue. This modeling method may help the data acquisition system structure to adapt to characteristics of input signals and features of communication lines in an output of system. [ABSTRACT FROM AUTHOR]

The versatility of simple electronic nodes and debugging of communication protocols in unlicensed frequency bands constitute the main reasons for the adoption of industrial wireless sensor networks (IWSNs). However, the industrial environments, in which IWSNs function, are characterized by a harsh impact of physical and electromagnetic factors that destabilize the operation of equipment and worsen the noise immunity of channels. To consider the effect of these two groups of factors on communication quality, we develop a novel model of an IWSN channel as the dependence of bit error probability (BEP) on signal distortions and interference impact. We found that direct sequence spread spectrum (DSSS) codes of a signal have different effects on the compensation of nonlinear signal distortions and external interference. Further, we propose an algorithm for the distribution of DSSS codes among real transmitters by BEP values in channels with known additive and multiplicative interference. Here, the distribution of codes is based on the minimax criterion. Finally, for cases when a signal is characterized by a-priori uncertainties of fading, we suggest an alternative algorithm for selecting a DSSS code based on Pareto bicriteria optimization. [Display omitted] • We model an IWSN channel with hardware signal distortion, fading and narrowband interference. • An algorithm for distributing DSSS codes among real transmitters minimizes BEP in the worst channel. • An algorithm for DSSS code selection with signal fading uncertainty reduces required computational costs. [ABSTRACT FROM AUTHOR]

[Display omitted] • We model wireless channels in terms of the hardware distortion of BFSK signals. • A complex indicator for the hardware distortion of signals is justified. • The table of energy consumption for nodes with various radio modules is calculated. • We propose a criterion for energy-efficient distribution of radio modules among nodes. Wireless sensor networks (WSNs) are widely used in various practical applications due to the availability of sensor and relay nodes. The imperfect technical condition of the modem part in nodes causes the hardware distortion of signals and the loss of messages in communication channels. Lost data recovery requires additional energy consumption, contributing to the premature discharge of batteries in individual nodes and decreased network lifetime. In this study, we propose an algorithm for the optimal energy-efficient distribution of radio modules (RMs) among the established node positions in terms of the technical condition of equipment using a minimax criterion. We developed physical layer models for the channels exhibiting incoherent reception of a priori distorted BFSK (binary frequency shift keying) signals under noise and Rayleigh and Rice fading conditions. Hardware distortions in these models are considered as a complex indicator represented by a coefficient showing similarity between the signal at the actual RM output and a reference signal with nominal parameters. The energy of the network is balanced due to the optimal distribution of the RMs among the node positions, taking into account the energy consumption of various WSN nodes. [ABSTRACT FROM AUTHOR]