Ant Study Could İnform Generation Of Computer Networks
Colonies could help scientists analyze social networks, coordinate robot swarms.
By Barry Eitel
SAN FRANCISCO – Engineers at Massachusetts Institute of Technology (MIT) announced Wednesday that they believe the next generation of massive computer networks could be based on something much smaller: ants.
By studying ant colonies and how the tiny insects communicate, researchers are gaining insights about how information travels between the creatures – research the MIT team believes will help develop the algorithms churning data around the world.
Ants are exceptionally good at estimating how many other ants are in a given location. The insects use these saturation estimates to decide when to move a colony to a new home. The MIT scientists found that the ants are such good guessers because they mentally chronicle each time they bump into other ants.
By using ant instincts as a guide, researchers believe they can form better computer networks that intuitively avoid bottlenecks and server slowdowns, among other advances.
The understanding has multiple applications, including analyzing social media sites, programming the decision making abilities of swarms of robots and organizing computerized sensors in treacherous environments like war zones or mountain ranges.
The MIT scientists made the announcement ahead of a presentation they will give later this month at the ACM Symposium on Principles of Distributed Computing Conference.
The randomness of how the ants explore the world is an important aspect of the new research. Since computers are, obviously, faster than ants, the scientists believe this model can be extrapolated to a worldwide scale.
“It's intuitive that if a bunch of people are randomly walking around an area, the number of times they bump into each other will be a surrogate of the population density,' said paper co-author Cameron Musco in a statement. “What we're doing is giving a rigorous analysis behind that intuition, and also saying that the estimate is a very good estimate, rather than some coarse estimate. As a function of time, it gets more and more accurate.'
Kaynak: AA
SAN FRANCISCO – Engineers at Massachusetts Institute of Technology (MIT) announced Wednesday that they believe the next generation of massive computer networks could be based on something much smaller: ants.
By studying ant colonies and how the tiny insects communicate, researchers are gaining insights about how information travels between the creatures – research the MIT team believes will help develop the algorithms churning data around the world.
Ants are exceptionally good at estimating how many other ants are in a given location. The insects use these saturation estimates to decide when to move a colony to a new home. The MIT scientists found that the ants are such good guessers because they mentally chronicle each time they bump into other ants.
By using ant instincts as a guide, researchers believe they can form better computer networks that intuitively avoid bottlenecks and server slowdowns, among other advances.
The understanding has multiple applications, including analyzing social media sites, programming the decision making abilities of swarms of robots and organizing computerized sensors in treacherous environments like war zones or mountain ranges.
The MIT scientists made the announcement ahead of a presentation they will give later this month at the ACM Symposium on Principles of Distributed Computing Conference.
The randomness of how the ants explore the world is an important aspect of the new research. Since computers are, obviously, faster than ants, the scientists believe this model can be extrapolated to a worldwide scale.
“It's intuitive that if a bunch of people are randomly walking around an area, the number of times they bump into each other will be a surrogate of the population density,' said paper co-author Cameron Musco in a statement. “What we're doing is giving a rigorous analysis behind that intuition, and also saying that the estimate is a very good estimate, rather than some coarse estimate. As a function of time, it gets more and more accurate.'