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