Inspiration from the fruit fly could simplify how
wireless sensor networks communicate
Over the years science has gleaned an enormous amount of
knowledge from the humble fruit fly. Drosophila melanogaster
was used to provide the post-Mendelian foundations for our
understanding of genetics and has also been used extensively in
neuroscience research. The latest fruit fly-inspired innovation
could simplify how wireless sensor networks communicate and
stands to have wider applications for computing.
This is not the first time computing systems have been
compared to biological systems. Learning from a comparison
between
Linux and E.coli and using
fly's eyes to help develop faster visual receivers for robots
are just two examples. This time round researchers at
Carnegie Mellon University (CMU), Pittsburgh, Pennsylvania,
have discovered a highly efficient system of organizing cells in
the fruit fly's nervous system develops that stands to have
applications in computer networking.
Without communication with surrounding cells or prior
knowledge of what these other cells are doing the fly's
developing nervous system is able to organize itself so that a
small number become leader cells or sensory organ precursor
cells (SOP), while the rest become ordinary nerve cells. The
SOPs which connect to adjoining nerve cells do not connect with
other SOPs, but instead to the ends of the nervous system that
are attached to tiny hairs for interacting with the outside
world. What is extraordinary about how this hierarchy of cells
organizes itself is the fact that the right number and
combination of SOP cells and nerve cells form without the need
for complicated information exchange.
The fly's nervous system uses a probabilistic method to
select the cells that will become SOPs. The cells have no
information about how they are connected to each other but as
various cells self-select themselves as SOPs, they send out
chemical signals to neighboring cells that inhibit those cells
from also becoming SOPs. This process continues for three hours,
until all of the cells are either SOPs or are neighbors to an
SOP, and the fly emerges from the pupal stage.
Ziv Bar-Joseph, associate professor of machine learning and
computational biology at CMU and author of the report noted that
the probability that any cell will self-select increases not as
a function of connections, as with a maximal independent set
(MIS) algorithm used in computer networking, but as a function
of time. The researchers believe that computer networks could be
developed using this innovative system creating networks which
are much simpler and more robust.
"It is such a simple and intuitive solution, I can't believe
we did not think of this 25 years ago," said co-author Noga
Alon, a mathematician and computer scientist at Tel Aviv
University and the Institute for Advanced Study in Princeton,
N.J.
Bar-Joseph, Alon and their co-authors – Yehuda Afek of
Tel Aviv University and Naama Barkai, Eran Hornstein and
Omer Barad of the
Weizmann
Institute of Science in Rehovot, Israel – developed a new
distributed computing algorithm using their findings. The
resulting network was shown to have qualities that are well
suited for networks in which the number and position of the
nodes is not completely certain including wireless sensor
networks, such as environmental monitoring, or where sensors are
dispersed. They also believe this could be used in systems for
controlling swarms of robots.
“The run time was slightly greater than current approaches,
but the biological approach is efficient and more robust because
it doesn't require so many assumptions," Bar-Joseph said. "This
makes the solution applicable to many more applications."
The research was supported in part by grants from the
National Institutes of Health and the
National Science
Foundation.
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