Open AccessSaturated distributed coordination function Markov model for noisy soft‐real‐time industrial wireless networks
Author(s) -
Maadani Mohsen,
Motamedi Seyed Ahmad,
Safdarkhani Hadi,
Parsa Mahshad
Publication year - 2014
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2013.0384
Subject(s) - computer science , distributed coordination function , network packet , markov chain , channel (broadcasting) , wireless network , frame (networking) , wireless , real time computing , markov process , packet loss , ieee 802.11 , computer network , telecommunications , statistics , mathematics , machine learning
The use of wireless technology in industrial networks is becoming more popular due to its numerous advantages. Providing an accurate model to study its performance parameters, is an essential step in designing a reliable and real‐time industrial wireless network protocol. Most of the existing models are accurate only for data networks which have large packet payloads and high signal to noise ratio (SNR) characteristics, and cannot be applied to study the performance of industrial networks which have short packet lengths and are used in harsh and noisy environments. In this paper a novel three‐dimensional discrete‐time Markov chain (DTMC) model has been proposed for the IEEE 802.11‐based industrial wireless networks using the distributed coordination function (DCF) as the medium access control (MAC) mechanism. It considers both causes of the backoff freezing: busy channel and the successive inter‐frame space (IFS) waiting phase. In this way, it provides a much more accurate estimation of the channel access and collision probabilities, resulting in more accurate network metric calculations. Also, based on the proposed model, a simple and accurate packet delay analysis is provided in the worst‐case saturated traffic and error‐prone channel condition which in comparison with similar works provides more accurate results.