Analytical model for CSMA-based MAC protocol for industrial IoT applications

The paper provides a method for calculating characteristics of a Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol to be used at the Medium Access Control (MAC) layer in an Industrial Internet of Things (IIoT) network. In particular, we consider a set of nodes equipped with sensors, deployed into an industrial machine, and having to transmit measured data to a final gateway. The gateway is equipped with multiple antennas and sweeps the entire area to gather data from the different nodes. A CSMA/CA protocol is used to limit interference among nodes. The mathematical model is based on an absorbing Discrete Time Markov Chain (DTMC) and the approach allows to estimate the average delay, the collision probability and the transmission probability by solving the system of transcendental equations. In addition, a queueing network method is also proposed for the modelling and compared to the absorbing DTMC solution. High accuracy of the absorbing DTMC method has been validated in numerical results by comparison with simulation results and with the queueing network method. The model obeys optimizing system parameters, such as the back-off time duration and other parameters of the CSMA/CA protocol. © Springer Nature Switzerland AG 2020.

Authors
Tsarev A. 1 , Khayrov E. 1 , Medvedeva E. 1, 2 , Gaidamaka Y. 1, 2 , Buratti C.3
Language
English
Pages
240-258
Status
Published
Volume
12526 LNCS
Year
2020
Organizations
  • 1 Peoples’ Friendship University of Russia (RUDN University), Moscow, Russian Federation
  • 2 Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Moscow, Russian Federation
  • 3 Wi-Lab, CNIT/DEI, University of Bologna, Bologna, Italy
Keywords
Average delay; Carrier sense multiple access with collision avoidance; Collision probability; Industrial Internet of Things

Other records

Adamu A., Platonova A., Yartseva I., Gaidamaka Y.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 12526 LNCS. 2020. P. 400-414