New Algorithm Aims to Solve Bottlenecks In Networks

By nocamels February 02, 2011 Comments

As sensors that do things like detect touch and motion in cell phones get smaller, cheaper and more reliable, computer manufacturers are beginning to take seriously the decade-old idea of “smart dust” — networks of tiny wireless devices that permeate the environment, monitoring everything from the structural integrity of buildings and bridges to the activity of live volcanoes. In order for such networks to make collective decisions, however — to, say, recognize that a volcano is getting restless — they need to integrate information gathered by hundreds or thousands of devices.

But networks of cheap sensors scattered in punishing and protean environments are prone to “bottlenecks,” regions of sparse connectivity that all transmitted data must pass through in order to reach the whole network. At the 2011 ACM-SIAM Symposium on Discrete Algorithms, which took place in New Orleans the weekend of Jan. 9, Keren Censor-Hillel, a postdoc at MIT’s Computer Science and Artificial Intelligence Laboratory, and Hadas Shachnai of Technion – Israel Institute of Technology – presented a new algorithm that handles bottlenecks much more effectively than its predecessors.

The algorithm is designed to work in so-called ad hoc networks, in which no one device acts as superintendent, overseeing the network as a whole. In a network of cheap wireless sensors, for instance, any given device could fail: its battery could die; its signal could be obstructed; it could even be carried off by a foraging animal. The network has to be able to adjust to any device’s disappearance, which means that no one device can have too much responsibility.

Without a superintendent, the network has no idea where its bottlenecks are. But that doesn’t matter to Censor-Hillel and Shachnai’s algorithm. “It never gets to identify the bottlenecks,” Censor-Hillel says. “It just copes with their existence.”

Consistent inconsistency

The researchers’ analysis of their algorithm makes a few simplifying assumptions that are standard in the field. One is that communication between networked devices takes place in rounds. Each round, a device can initiate communication with only one other device, but it can exchange an unlimited amount of information with that device and with any devices that contact it. During each exchange, it passes along all the information it’s received from any other devices. If the devices are volcano sensors, that information could be, say, each device’s most recent measurement of seismic activity in its area.

It turns out that if you’re a sensor in a network with high connectivity — one in which any device can communicate directly with many of the others — simply selecting a neighboring device at random each round and sending it all the information you have makes it likely that every device’s information will permeate the whole network. But take two such highly connected networks and connect them to each other with only one link — a bottleneck — and the random-neighbor algorithm no longer works well. On either side of the bottleneck, it could take a long time for information to work its way around to the one device that communicates with the other side, and then a long time for that device to bother to send its information across the bottleneck.

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Photo by Mauro Bieg

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