The Impact of Timing in Data Aggregation for Sensor Networks
Ignacio Solis and Katia Obraczka
To appear ICC 04
Abstract
This paper evaluates the effect of timing in data aggregation
algorithms. In-network aggregation achieves energy-efficient data
propagation by processing data as it flows from information sources to
sinks. Our goal is to show that the decision of when to "clock out"
data as it is processed by nodes have significant performance impact
in terms of data accuracy and freshness. Using the sensor network
paradigm where all nodes produce information periodically, we compare
three aggregation timing policies. Through extensive simulations we
show that setting up the clock out timer based on a node's position in
the aggregation tree results in a beneficial "cascading effect",
yielding considerable energy efficiency, yet maintaining data accuracy
and freshness.
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The authors can be reached at
isolis@cse.ucsc.edu and
katia@cse.ucsc.edu.
This page was last modified Mar/18/2004