V. Pryyma, L. Bölöni, and D. Turgut

Active time scheduling for rechargeable sensor networks


Cite as:

V. Pryyma, L. Bölöni, and D. Turgut. Active time scheduling for rechargeable sensor networks. Computer Networks (Elsevier), 54(4):631–640, March 2010.

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Abstract:

Recent progress in energy harvesting technologies made it possible to build sensor networks with rechargeable nodes which target an indefinitely long operation. In these networks, the goal of energy management is to allocate the available energy such that the important performance metrics, such as the number of detected threats, are maximized. As the harvested energy is not sufficient for continuous operation, the scheduling of the active and inactive time is one of the main components of energy management. The active time scheduling protocols need to maintain the energy equilibrium of the nodes, while considering the uncertainties of the energy income, which is strongly influenced by the weather, and the energy expenditures, which are dependent on the behavior of the targets. In this paper, we describe and experimentally compare three active time scheduling protocols: (a) static active time, (b) dynamic active time based on multi-parameter heuristic and and (c) utility-based uniform sensing. We show that protocols which take into consideration the probabilistic models of the energy income and expenditure and can dynamically adapt to changes in the environment, can provide a significant performance advantage

BibTeX:

@article{Pryyma-2010-ComNet,
      author= "V. Pryyma and L. B{\"o}l{\"o}ni and D. Turgut",
      title= "Active time scheduling for rechargeable sensor networks",
      journal ="Computer Networks (Elsevier)",
      year = "2010",
      month = "March",
      volume = "54",
      number = "4",
      pages = "631-640",
      abstract = {
      Recent progress in energy harvesting technologies made it possible
      to build sensor networks with rechargeable nodes which target an
      indefinitely long operation. In these networks, the goal of energy
      management is to allocate the available energy such that the
      important performance metrics, such as the number of detected
      threats, are maximized. As the harvested energy is not sufficient
      for continuous operation, the scheduling of the active and
      inactive time is one of the main components of energy management.
      The active time scheduling protocols need to maintain the energy
      equilibrium of the nodes, while considering the uncertainties of
      the energy income, which is strongly influenced by the weather,
      and the energy expenditures, which are dependent on the behavior
      of the targets. In this paper, we describe and experimentally
      compare three active time scheduling protocols: (a) static active
      time, (b) dynamic active time based on multi-parameter heuristic
      and and (c) utility-based uniform sensing. We show that protocols
      which take into consideration the probabilistic models of the
      energy income and expenditure and can dynamically adapt to changes
      in the environment, can provide a significant performance
      advantage
      },
}

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