• DocumentCode
    1649802
  • Title

    Nodes Localization Algorithm for Linear Wireless Sensor Networks in Underground Coal Mine Based on RSSI-Similarity Degree

  • Author

    Tian, Feng ; Dong, Ying ; Sun, Enyan ; Wang, Chuanyun

  • Author_Institution
    Sch. of Comput. Sci., Shenyang Aerosp. Univ., Shenyang, China
  • fYear
    2011
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    Aiming at the problems that non-line-measurement error is easily produced in narrow roadway and RSSI values are seriously influenced by underground coal mine environment, a dynamic a localization algorithm based on RSSI-similarity degree(DaLA-RSD) is proposed. The algorithm uses the information between tested beacon nodes group and the other side´s two adjacent beacon nodes groups to get path loss index a of each location, in order to reduce the non-line-measurement error and obtain accurate measurement of distance. On the basis of this case, desired beacon nodes are selected to locate by RSSI-similarity degree and group number. Simulation results show that the algorithm has higher localization accuracy than maximum likelihood estimation localization algorithm(MLELA).
  • Keywords
    coal; distance measurement; maximum likelihood estimation; mining; wireless sensor networks; MLELA; RSSI-similarity degree; beacon nodes group; distance measurement; linear wireless sensor networks; maximum likelihood estimation localization algorithm; narrow roadway; node localization algorithm; nonline-measurement error; underground coal mine; Coal mining; Heuristic algorithms; Indexes; Maximum likelihood estimation; Mobile communication; Signal processing algorithms; Wireless sensor networks;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Wireless Communications, Networking and Mobile Computing (WiCOM), 2011 7th International Conference on
  • Conference_Location
    Wuhan
  • ISSN
    2161-9646
  • Print_ISBN
    978-1-4244-6250-6
  • Type

    conf

  • DOI
    10.1109/wicom.2011.6040339
  • Filename
    6040339