Reporting Rare Events in Sensor Networks
Computing and maintaining network structures for energy
efficient data aggregation incurs high overhead for dynamic events where the set of nodes sensing an event
changes with time. Prior works on data aggregation protocols have focused on tree-based or cluster-based
structured approaches. Although structured approaches are suited for data gathering applications, they incur
high maintenance overhead in dynamic scenarios for event-based applications.
We have proposed the first structure-free data aggregation technique that achieves high energy efficiency.
As the proposed technique is opportunistic, the performance can be arbitrarily bad. We have proposed a new
approach that can provide guarantees for efficient aggregation. It leverages an implicitly computed Directed
Acyclic Graph (DAG) to forward packets. Specifically, a routing tree is chosen on the DAG in a dynamic
fashion depending on the location of the data sources. This protocol was shown to aggregate data within
a constant distance from the sources under the limitation of a maximum size for any event. We have lately
relaxed this condition using a different data structure based on the quad-tree. Specifically, the data structure
uses a quad-tree and an overlapping alternative tree structure. Dynamic forwarding decisions between the two
trees can ensure aggregation within a constant distance from the data sources, irrespective of the event-size.
As this distance is independent of the network size, the approach is scalable. The performance of our solutions
for data aggregation have been studied using simulations and experimentation on the Kansei testbed at OSU.
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