MoBAN: a configurable mobility model for wireless body area networks

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

60 Citations (Scopus)
3 Downloads (Pure)

Abstract

A good mobility model is an essential prerequisite for performance evaluation of protocols for wireless networks with node mobility. Sensor nodes in a Wireless Body Area Network (WBAN) exhibit high mobility. The WBAN topology may completely change because of posture changes and movement even within a certain type of posture. The WBAN also moves as a whole in an ambient network. Therefore, an appropriate mobility model is of great importance for performance evaluation. This paper presents a comprehensive configurable mobility model MoBAN for evaluating intra-and extra-WBAN communication. It implements different postures as well as individual node mobility within a particular posture. The model can be adapted to a broad range of applications for WBANs. The model is made available through http://www.es.ele.tue.nl/nes/, as an add-on to the mobility framework of the OMNeT++ simulator. Two case studies illustrate the use of the mobility model for performance evaluation of network protocols.
Original languageEnglish
Title of host publicationProceedings of the 4th International Conference on Simulation Tools and Techniques (SIMUTools 2011), 21-25 March 2011, Barcelona, Spain
Place of PublicationBrussels, Belgium
PublisherICST
Pages168-177
ISBN (Print)978-1-936968-00-8
Publication statusPublished - 2011
Event4th International Conference on Simulation Tools and Techniques (SIMUTools 2011), March 21-25, 2011, Barcelona, Spain - Barcelona, Spain
Duration: 21 Mar 201125 Mar 2011

Conference

Conference4th International Conference on Simulation Tools and Techniques (SIMUTools 2011), March 21-25, 2011, Barcelona, Spain
Abbreviated titleSIMUTools 2011
Country/TerritorySpain
CityBarcelona
Period21/03/1125/03/11

Fingerprint

Dive into the research topics of 'MoBAN: a configurable mobility model for wireless body area networks'. Together they form a unique fingerprint.

Cite this