Fruit Fly Nervous System Can Pave Way for Wireless Sensor Networks

by Sheela Philomena on  January 17, 2011 at 12:22 PM Research News   - G J E 4
A recent study conducted in fruit flies revealed that the fruit flies nervous system could be used to effectively deploy wireless sensor networks and other distributed computing applications.
 Fruit Fly Nervous System Can Pave Way for Wireless Sensor Networks
Fruit Fly Nervous System Can Pave Way for Wireless Sensor Networks

Ziv Bar-Joseph at Carnegie Mellon University found that the cells in the fly's developing nervous system manage to organize themselves so that a small number of cells serve as leaders that provide direct connections with every other nerve cell.

The find indicates similar techniques used to manage the distributed computer networks. However, the fly's nervous system techniques are much simpler and more robust.

Using the fruit fly technique, Bar-Joseph, co-author Noga Alon and their team designed a new distributed computing algorithm, which is particularly well suited for wireless sensor networks, such as environmental monitoring, where sensors are dispersed in a lake or waterway, or systems for controlling swarms of robots.

"Computational and mathematical models have long been used by scientists to analyze biological systems," said Bar-Joseph.

"Here we've reversed the strategy, studying a biological system to solve a long-standing computer science problem."

One step toward creating this distributive system is to find a small set of processors that can be used to rapidly communicate with the rest of the processors in the network - what graph theorists call a maximal independent set (MIS).

Every processor in such a network is either a leader (a member of the MIS) or is connected to a leader, but the leaders are not interconnected.

The researchers created a computer algorithm based on the fly's approach and proved that it provides a fast solution to the MIS problem.

"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."

Source: ANI

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