• DocumentCode
    3225747
  • Title

    Utilising mobile phone RSSI metric for human activity detection

  • Author

    Doyle, J. ; Farrell, R. ; McLoone, S. ; McCarthy, T. ; Tahir, M. ; Hung, P.

  • Author_Institution
    Inst. of Microelectron. & Wireless Syst., Nat. Univ. of Ireland Maynooth, Maynooth, Ireland
  • fYear
    2009
  • fDate
    10-11 June 2009
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    Recent research into urban analysis through the use of mobile device usage statistics has presented a need for the collection of this data independently from mobile network operators. In this paper we propose that cumulative received signal strength indications (RSSI) for overall mobile device transmissions in an area may provide such independent information. A process for the detection of high density areas within the RSSI temporal data set will be demonstrated. Finally, future applications for this collection method are discussed and we highlight its potential to complement traditional metric analysis techniques, for the representation of intensity of urban and local activities and their evolution through time and space.
  • Keywords
    geographic information systems; mobile communication; mobile handsets; wireless sensor networks; geographical mapping; human activity detection; metric analysis techniques; mobile communications; mobile phone RSSI metric; received signal strength indications; temporal analysis; Erlang; Mobile communications; RSSI; geographical mapping; human activity; temporal analysis; urban analysis;
  • fLanguage
    English
  • Publisher
    iet
  • Conference_Titel
    Signals and Systems Conference (ISSC 2009), IET Irish
  • Conference_Location
    Dublin
  • Type

    conf

  • DOI
    10.1049/cp.2009.1728
  • Filename
    5524669