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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