Continuous approximation of stochastic models for wireless sensor networks

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Stochastic analysis of wireless sensor networks becomes exceedingly hard as the number of nodes in a network grows large. In this paper we intend to address this issue by proposing a method of modeling large networks by dynamical systems rather than explicit Markov models, called Mean-Field Approximation. We verify the suitability of Mean-Field Approximation by analyzing ALOHA, by both studying a discrete model and a system of differential equations and then by comparing these models. We then extend the modeling technique in order to express characteristics of a network running a CSMA/CA protocol.
Original languageEnglish
Title of host publication2015 IEEE Symposium on Communications and Vehicular Technology in the Benelux (SCVT), 24 November 2015, Luxembourg City
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Number of pages6
Publication statusPublished - 24 Nov 2015


  • Analytical models
  • Approximation methods
  • Markov processes
  • Mathematical model
  • Protocols
  • Receivers
  • Wireless sensor networks


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