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Bootstrapping a Hop-Optimal Network in the Weak Sensor Model
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Bootstrapping a Hop-Optimal Network in the Weak Sensor Model
Martín Farach-Colton1 , Rohan J. Fernandes1 and Miguel A. Mosteiro1 
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Department of Computer Science, Rutgers University, Piscataway, NJ 08854, USA |
Abstract
Sensor nodes are very weak computers that get distributed at random on a surface. Once deployed, they must wake up and form
a radio network. Sensor network bootstrapping research thus has three parts: one must model the restrictions on sensor nodes;
one must prove that the connectivity graph of the sensors has a subgraph that would make a good network; and one must give
a distributed protocol for finding such a network subgraph that can be implemented on sensor nodes.
Although many particular restrictions on sensor nodes are implicit or explicit in many papers, there remain many inconsistencies
and ambiguities from paper to paper. The lack of a clear model means that solutions to the network-bootstrapping problem in
both the theory and systems literature all violate constraints on sensor nodes. For example, random geometric graph results
on sensor networks predict the existence of subgraphs on the connectivity graph with good route-stretch, but these results
do not address the degree of such a graph, and sensor networks must have constant degree. Furthermore, proposed protocols
for actually finding such graphs require that nodes have too much memory, whereas others assume the existence of a contention-resolution
mechanism.
We present a formal Weak Sensor Model that summarizes the literature on sensor node restrictions, taking the most restrictive choices when possible. We show that
sensor connectivity graphs have low-degree subgraphs with good hop-stretch, as required by the Weak Sensor Model. Finally, we give a Weak Sensor Model-compatible protocol for finding such graphs. Ours is the first network initialization algorithm that is implementable on
sensor nodes.
This research was supported in part by DIMACS, Center for Discrete Mathematics & Theoretical Computer Science, grants numbered
NSF CCR 00-87022, NSF EIA 02-05116 and Alfred P. Sloan Foundation 99-10-8.
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