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, 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 a multi-parameter heuristic 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)",
      volume = "54",
      number = "4",
      pages = "631-640",
      year = "2010",
      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 a multi-parameter heuristic 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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