The impact of timing in data aggregation for sensor networks

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.