• DocumentCode
    2208730
  • Title

    Automata based energy efficient spanning tree for data aggregation in Wireless Sensor Networks

  • Author

    Eskandari, Zahra ; Yaghmaee, Mohammad Hossien ; Mohajerzadeh, AmirHossien

  • Author_Institution
    Dept. of Comput. Eng., Ferdowsi Univ. of Mashhad, Mashhad, Iran
  • fYear
    2008
  • fDate
    19-21 Nov. 2008
  • Firstpage
    943
  • Lastpage
    947
  • Abstract
    Wireless sensor networks (WSNs) are networks that consist of low power nodes with limited processing ability. In some applications, these sensor nodes sense data from the environment periodically and transmit these data to sink node. In order to increase network¿s lifetime, number of transmitted data packet should be minimized. A solution which is suggested for decreasing transmitted data volume is aggregation. Aggregation algorithms should construct aggregation tree and transmit data to sink based on this tree. In this paper, we propose new learning automata based aggregation protocol which called automata energy efficient spanning tree, AEEspan. Simulation results show that the proposed algorithm has better performance in energy efficiency which leads to higher lifetime.
  • Keywords
    data communication; learning automata; protocols; telecommunication computing; trees (mathematics); wireless sensor networks; AEEspan; automata based energy efficient spanning tree; data aggregation; learning automata; low power nodes; protocol; sensor nodes sense data; sink node; wireless sensor networks; Computer networks; Data engineering; Electronic mail; Energy efficiency; Learning automata; Power engineering and energy; Power engineering computing; Protocols; Routing; Wireless sensor networks; Automata learning; Data Aggregation; Energy efficient; Spanning Tree; Wireless Sensor Networks;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Communication Systems, 2008. ICCS 2008. 11th IEEE Singapore International Conference on
  • Conference_Location
    Guangzhou
  • Print_ISBN
    978-1-4244-2423-8
  • Electronic_ISBN
    978-1-4244-2424-5
  • Type

    conf

  • DOI
    10.1109/ICCS.2008.4737323
  • Filename
    4737323