• DocumentCode
    3017525
  • Title

    Detecting termination in pervasive sensor networks

  • Author

    Kurian, Habel ; Rakshit, Abhishek ; Singh, Gurdip

  • Author_Institution
    Comput. & Inf. Sci., Kansas State Univ., Manhattan, KS, USA
  • fYear
    2009
  • fDate
    23-25 March 2009
  • Firstpage
    1
  • Lastpage
    8
  • Abstract
    With the increased deployment of pervasive systems, there has been an explosive growth in the number of applications being developed for such systems. Distributed applications typically depend on the underlying middleware infrastructure to provide services to perform their tasks. Many applications rely on a service which can detect the termination of a distributed activity being performed by a set of entities. Existing algorithms for termination detection are based on the layering paradigm wherein the algorithm can monitor application level communication. Pervasive applications, however, may not be structured as strictly layered systems. This paper proposes algorithms for termination detection of distributed applications in pervasive systems. We propose two algorithms for this problem, and show that each performs better than the other under certain conditions. Subsequently, we propose an hybrid algorithm which combines the features of the two algorithms and provides performance comparable to the better of the two algorithms under different conditions.
  • Keywords
    distributed sensors; application level communication; detecting termination; pervasive sensor networks; pervasive systems; Computer networks; Computerized monitoring; Detection algorithms; Explosives; Fires; Manufacturing automation; Middleware; Military computing; Pervasive computing; Sensor systems and applications;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Autonomous Decentralized Systems, 2009. ISADS '09. International Symposium on
  • Conference_Location
    Athens
  • Print_ISBN
    978-1-4244-4327-7
  • Type

    conf

  • DOI
    10.1109/ISADS.2009.5207330
  • Filename
    5207330