This is
a concept, which draws both from my work experience with a Big data analytic
database company at the research park and the impressions of observing ant
behavior during the entomology lab visit as part of the bio-creativity class.
The ant
colonies have always interested me; they are millions of ants living together
communicating effectively to fulfill all the different activities. Work specialization and communication are the prime attributes of their
social behavior.
These two themes made me think of how we can design intelligent networks
that can route data packets, load balance and conserve energy by exchanging
information via signaling. All the stimuli that ants typically use to
communicate can be mimicked in the form of data signals. These signals can be analyzed
in real time by using big data (machine data) analytics technology. The
overlying logical layer can help make real time decisions.
Starting
with this idea of machine to machine communication modeled along the line of
communication in ant societies I tried to look for existing research in this
space. I came across the idea of studying ant behavior to make social networks
better. A new study from the University of Madrid suggests that the behavior of
real-life ants could inspire developers to improve the way these sites
function. By constructing an algorithm modeled off the foraging behavior of
ants, researchers are hoping to accelerate the way relationships are
established in social networks.
Ants have evolved extremely
sophisticated behaviors when it comes to looking for food. Their foraging
techniques have been delicately refined through the processes of natural
selection — and now, researchers want to tap into that biological insight in an
effort to develop an algorithm that could vastly improve the efficiency of any
kind of software that needs to make quick associations between related
elements.
Biologists know that ants utilize
a kind of biological algorithm to perform a similar task when they're looking
for food. Specifically, ants are capable of finding the path between the
anthill and the source of food by secreting and following a chemical trail,
called a pheromone,
which is deposited on the ground. In the ant world, ants catch a sniff of other
scented trails, allowing them to follow both the pheromone as well as the scent
of food helping them to find the food more quickly.
In
the true vein of improvisation, during our group discussion in class, we
discussed the concept of food networks. An integrated network of producers each
specialized in an area of production with a strong communication network which
can provide real time feedback and hence help mitigate any food shortage risks.
However unlike ant colonies which are fiercely competitive between themselves,
the human food networks could depart from this behavior and be more
collaborative.
I
understand that we already have some really intelligent networks in existence
which imbibe the attributes that I have discussed in my paper. However I found
it interesting to draw parallels between the human networks and the ant
colonies. I feel that ant colonies are like a bio-model and a better
understanding of them can help us refine our network solutions. I am excited
about the idea of using ant wisdom in the design of both our machine to machine
networks, social networks and networks that furnish our daily needs-(food,
water) to usher in a more intelligent networking space.
References:
How Studying Ant Behavior Can
Make Social Networks Better
The Behavior of Ants
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