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Fruit fly reveals secrets of computing

Nervous system solves networking problem
Mon Jan 17 2011, 15:05

ROTTING APPLES can ooze a sigh of relief as apparently fruit flies are busy working as networking systems researchers.

According to white-coated insect wranglers, the humble fruit fly can provide a clue to one of man's greatest challenges - how best to work out MIS (maximal independent set) IT network problems.

Research from Carnegie Mellon University suggests that the fruit fly's nervous system has a tiny hair like structure that could be applied to wireless sensor networks and distributed computing.

"With a minimum of communication and without advance knowledge of how they are connected with each other, the cells in the fly's developing nervous system manage to organise themselves so that a small number of cells serve as leaders that provide direct connections with every other nerve cell", said author Ziv Bar-Joseph, associate professor of machine learning at Carnegie Mellon University.

The report's co-author Noga Alon, a mathematician and computer scientist at Tel Aviv University, added that mimicking how a fly behaves could help us humans control a plane in flight, for example. Which is fine, just as long as it does not mean that planes start landing on baskets of fruit.

"It is such a simple and intuitive solution, I can't believe we did not think of this 25 years ago," he said.

According to the report, the way the fruit fly's bristles connect to the outside world appeals to researchers, as opposed to the way that the insects dispose of apples in the workplace. Each of the bristles can work independently, they said, and function as a leader helping the fly to make decisions on the, er, fly.

Aping the fly will remove the element of chance from networking at its most efficient, which was described as being similar to rolling dice. To prove their point the researchers created an algorithm based on the fly way of doing things and found that indeed it did improve the situation.

"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," explained Bar-Joseph. "This makes the solution applicable to many more applications." µ



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