Sensor networks are especially useful in catastrophic or emergency
scenarios such as floods, fires, terrorist attacks or earthquakes
where human participation may be too dangerous. However, such
disaster scenarios pose an interesting design challenge since the
sensor nodes used to collect and communicate data may themselves
fail suddenly and unpredictably, resulting in the loss of valuable
data. Furthermore, because these networks are often expected to be
deployed in response to a disaster, or because of sudden
configuration changes due to failure, these networks are often
expected to operate in a "zero-configuration" paradigm, where data
collection and transmission must be initiated immediately, before
the nodes have a chance to assess the current network topology. In
this paper, we design and analyze techniques to increase
"persistence" of sensed data, so that data is more likely to reach
a data sink, even as network nodes fail. This is done by replicating
data compactly at neighboring nodes using novel "Growth Codes"
that increase in efficiency as data accumulates at the sink.
We show that Growth Codes preserve more data in the presence of node
failures than previously proposed erasure resilient techniques.
Joint work with Jon Feldman, Vishal Misra and Dan Rubenstein.
Abhinav Kamra is a PhD student with the Department of Computer Science
at Columbia University in New York. He obtained his B.Tech. in
Computer Science from IIT Delhi in India and his M.S. in Computer Science
from Columbia University. His research interests include Network Coding
and its Applications in Sensor Networks, Performance Evaluation and
Design of Algorithms for Communication Networks. He is supervised in his
research by Prof. Vishal Misra and Prof. Dan Rubenstein.