D. Turgut and L. Bölöni

Heuristic approaches for transmission scheduling in sensor networks with multiple mobile sinks


Cite as:

D. Turgut and L. Bölöni. Heuristic approaches for transmission scheduling in sensor networks with multiple mobile sinks. The Computer Journal, 54(3):332–344, Oxford University Press, March 2011.

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Abstract:

A large part of the energy budget of traditional sensor networks is consumed by the hop-by-hop routing of the collected information to the static sink. In many applications it is possible to replace the static sink with one or more mobile sinks which move in a sensor field and collect the data through one hop transmissions. This greatly reduces the power consumption of the nodes, which can be further reduced by choosing the appropriate moment of transmission. In general, the transmission energy increases quickly with the distance, thus it makes sense for the nodes to transmit when one of the mobile sinks is in close proximity. Seeing the node as an autonomous agent, it needs to choose its actions of transmitting or buffering the collected data based on what it knows about the environment and its predictions about the future. The sensor agent needs to appropriately balance two objectives: the maximization of the utility of the collected and transmitted data and the minimization of the energy expenditure. We introduce the cummulative policy penalty (CPP) as an expression of this interdependent pair of requirements. As a baseline, we describe a graph-theory based approach for calculating the optimal policy in a complete knowledge setting. Then, we describe and compare three heuristics based on different principles (imitation of human decision making, stochastic transmission and constant risk). We compare the proposed approaches in an experimental study under a variety of scenarios.

BibTeX:

@article{Turgut-2011-CompJournal,
    author = "D. Turgut and L. B{\"o}l{\"o}ni",
    title = "Heuristic approaches for transmission scheduling in sensor networks with multiple mobile sinks",
    journal = "The Computer Journal",
    publisher = "Oxford University Press",
    year = "2011",
    volume = "54",
    number = "3",
    pages = "332--344",
    month = "March",
    html_dl_pdf = "http://www.eecs.ucf.edu/~lboloni/Publications/Download/Turgut-2009-CompJournal.pdf",
    abstract = {
      A large part of the energy budget of traditional sensor networks
      is consumed by the hop-by-hop routing of the collected information
      to the static sink. In many applications it is possible to replace
      the static sink with one or more mobile sinks which move in a
      sensor field and collect the data through one hop transmissions.
      This greatly reduces the power consumption of the nodes, which can
      be further reduced by choosing the appropriate moment of
      transmission. In general, the transmission energy increases
      quickly with the distance, thus it makes sense for the nodes to
      transmit when one of the mobile sinks is in close proximity.
      Seeing the node as an autonomous agent, it needs to choose its
      actions of transmitting or buffering the collected data based on
      what it knows about the environment and its predictions about the
      future. The sensor agent needs to appropriately balance two
      objectives: the maximization of the utility of the collected and
      transmitted data and the minimization of the energy expenditure.
      We introduce the cummulative policy penalty (CPP) as an expression
      of this interdependent pair of requirements. As a baseline, we
      describe a graph-theory based approach for calculating the optimal
      policy in a complete knowledge setting. Then, we describe and
      compare three heuristics based on different principles (imitation
      of human decision making, stochastic transmission and constant
      risk). We compare the proposed approaches in an experimental study
      under a variety of scenarios.
    }
}

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