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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