Collision resolution simulation for distributed control architectures using LonWorks

Mianyu Wang, Erwei Lin, Edward Woertz, Moshe Kam

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Local Operating Networks (LonWorks) is a widely used industrial technology applied to distributed control, sensor, and actuator networks. LonWorks is based on ANSI/EIA standard 709.1, and uses the predictive p-persistent CSMA algorithm in the MAC sublayer for collision resolution and decentralized traffic control. In spite of its popularity, only a few design tools are available to simulate LonWorks architectures and predict their performance. In this paper, we describe a simulation model for the collision resolution algorithm of LonWorks, based on the OPNET Modeler. We present the design framework, and develop a state variable representation and state transition diagrams that allow accurate simulation of performance and prediction of the algorithm's behavior. To validate our model, we compared its predictions to measurements from a physical LonWorks testbed and a Markov chain analytical model, and demonstrated a high level of agreement.

Original languageEnglish (US)
Title of host publicationProceedings of the 2005 IEEE Conference on Automation Science and Engineering, IEEE-CASE 2005
Pages319-326
Number of pages8
DOIs
StatePublished - 2005
Externally publishedYes
Event2005 IEEE Conference on Automation Science and Engineering, IEEE-CASE 2005 - Edmonton, Canada
Duration: Aug 1 2005Aug 2 2005

Publication series

NameProceedings of the 2005 IEEE Conference on Automation Science and Engineering, IEEE-CASE 2005
Volume2005

Other

Other2005 IEEE Conference on Automation Science and Engineering, IEEE-CASE 2005
Country/TerritoryCanada
CityEdmonton
Period8/1/058/2/05

All Science Journal Classification (ASJC) codes

  • General Engineering

Fingerprint

Dive into the research topics of 'Collision resolution simulation for distributed control architectures using LonWorks'. Together they form a unique fingerprint.

Cite this