• DocumentCode
    1772849
  • Title

    On the heavy tail properties of spatial node density for realistic mobility modeling

  • Author

    Ferreira, Danielle Lopes ; Nunes, Bruno A. A. ; Obraczka, Katia

  • Author_Institution
    Dept. of Comput. Eng., Univ. of California, Santa Cruz, Santa Cruz, CA, USA
  • fYear
    2014
  • fDate
    June 30 2014-July 3 2014
  • Firstpage
    504
  • Lastpage
    512
  • Abstract
    In this paper, we show empirically that the spatial node density resulting from human mobility follows a power law. We also show that the number of locales visited by users also exhibit heavy-tail behavior. We develop a stochastic model that confirms our empirical observations by showing that node mobility resulting from our model closely approximates mobility recorded in real traces collected from a variety of scenarios. Besides corroborating our empirical observations, we showcase another application of our model by using it to generate mobility regimes whose spatial node density exhibit heavy-tail behavior. We validate the resulting mobility generator by comparing its output against real traces.
  • Keywords
    mobile radio; stochastic processes; heavy tail properties; heavy-tail behavior; human mobility; mobility regimes; node mobility; power law; realistic mobility modeling; spatial node density; stochastic model; Analytical models; Conferences; Educational institutions; Global Positioning System; Mathematical model; Sensors; Stochastic processes;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Sensing, Communication, and Networking (SECON), 2014 Eleventh Annual IEEE International Conference on
  • Conference_Location
    Singapore
  • Type

    conf

  • DOI
    10.1109/SAHCN.2014.6990389
  • Filename
    6990389