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CMU-CS-03-112
Computer Science Department
School of Computer Science, Carnegie Mellon University
CMU-CS-03-112
GEM: Graph EMbedding for Routing and Data-Centric Storage
in Sensor networks without Geographic Information
James Newsome*, Dawn Song*
March 2003
(Release: February 2003)
CMU-CS-03-112.ps
CMU-CS-03-112.pdf
Keywords: Sensor networks, routing, data-centric storage
The widespread deployment of sensor networks is on the horizon.
One of the main challenges in sensor networks is to process and
aggregate data in the network rather than wasting energy by sending
large amounts of raw data to reply to a query. Some efficient data
dissemination methods, particularly data-centric storage and information
aggregation, rely on efficient routing from one node to another. In
this paper we introduce GEM (Graph EMbedding for sensor networks),
an infrastructure for node-to-node routing and data-centric storage
and information processing in sensor networks. Unlike previous
approaches, it does not depend on geographic information, and it
works well even in the face of physical obstacles. In GEM, we
construct a labeled graph that can be embedded in the original
network topology in an efficient and distributed fashion. In that
graph, each node is given a label that encodes its position in the
original network topology. This allows messages to be efficiently
routed through the network, while each node only needs to know the
labels of its neighbors. To demonstrate how GEM can be applied,
we have developed a concrete graph embedding method, VPCS
(Virtual Polar Coordinate Space). In VPCS, we embed a ringed tree
into the network topology, and label the nodes in such a manner as
to create a virtual polar coordinate space. We have also developed
VPCR, an efficient routing algorithm that uses VPCS. VPCR is the
first algorithm for node-to-node routing that guarantees reachability,
requires each node to keep state only about its immediate neighbors,
and requires no geographic information. Our simulation results show
that VPCR is robust on dynamic networks, works well in the face
of voids and obstacles, and scales well with network size and density.
27 pages
*Department of Electrical and Computer Engineering, Carnegie Mellon University.
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