Sensor network mimics synchronized calling by frogs and cicadas

The modern world is filled with the uncoordinated beeping and buzzing of countless electronic devices. So it was only a matter of time before someone designed an electronic network with the ability to synchronize dozens of tiny buzzers, in much the same way that frogs and cicadas coordinate their night-time choruses.

“Several years ago I was on a camping trip and we pitched our tent in an area that was filled with hundreds of tree frogs,” says Kenneth D. Frampton, an assistant professor of mechanical engineering at Vanderbilt University, who dreamed up the project. “The frogs were so loud that I couldn’t get to sleep. So I began listening to the chorus and was fascinated by how the pattern of synchronized calling moved around: Frogs in one area would croak all together for a while, then gradually one group would develop a different rhythm and drift off on its own.”

Last summer’s emergence of cicada brood X brought back that memory and prompted Frampton to assign undergraduates Efosa Ojomo and Praveen Mudindi–working under the supervision of graduate student Isaac Amundson–with the task of simulating this complex natural behavior using a wireless distributed sensor network. They presented the results of their project on Nov. 16 at the annual meeting of the American Acoustical Society in San Diego.

Consulting the literature about animal vocalizations, the engineers discovered that a number of different theories have been advanced to explain such naturally occurring synchronized behaviors. They may have evolved cooperatively in order to maximize signal loudness, to confuse predators or to improve call features that attract potential mates. Or they may have evolved competitively in order to mask or jam the calls of nearby animals. “Whichever theory is true, it is clear that these behavior patterns are complex and offer an interesting inspiration for group behaviors,” says Frampton.

One thing that these behaviors have in common is that they are produced by groups of animals who are in communication with each other but who are acting on their own. Networks consisting of nodes that communicate with each other but act independently according to simple rules are becoming increasingly popular and were the obvious system to use. “There is a great deal that we do not yet know about the group behavior of such systems,” says Frampton. “So, in addition to being a lot of fun, the synchronized calling experiment is adding to our understanding of the behavior of this kind of network.”

The engineers began with a wireless network of 15 to 20 “Motes,” a wireless network designed by computer scientists at the University of California, Berkeley and manufactured commercially by Crossbow Inc. These are small microprocessors equipped with wireless communications. The researchers added a microphone and a buzzer to each node.

To mimic synchronized calling behaviors, the researchers first programmed a single leader, dubbed the alpha node, to begin calling (buzzing) with an arbitrary duration and frequency. The alpha node was set so it called at this rate regardless of any other calling in its vicinity. The remainder of the devices, referred to as beta nodes, were programmed differently. They were instructed to listen with their microphones and when they hear a call that is sufficiently loud, to estimate its duration and frequency and then begin calling in synch with the detected call. “Although this behavioral algorithm is quite simple, it produces some interesting group behaviors,” Frampton reports.

When all is quiet and an alpha node begins calling, at first only those beta nodes nearby hear the call and respond. Then, as more betas swell the chorus, nodes farther away hear the call and join in. In this fashion, synchronized calling gradually spreads concentrically out from the alpha node until all the nodes are synchronized.

A second interesting behavior occurs when a beta node “hiccups” and starts buzzing out of synch with its neighbors. Such hiccups can be caused by measurement noise, operating system jitter and other factors. Occasionally, when such a hiccup occurs, neighboring nodes resynchronize to the errant node. Normally, these transients quickly disappear as the wayward group resynchronizes with the larger group.

The most interesting behavior pattern appeared when the researchers introduced a third kind of node that they labeled omega. This node was programmed identically to an alpha node but set to a different duration and frequency. When introduced into the array, an omega node begins to attract neighboring nodes to its call cycle. Unlike the hiccup case, however, the omega group does not resynchronize with the original group. Rather, the omega node eventually recruits a growing number of nodes to its calling cycle until a “balance of power” is reached with the alpha node. The eventual balance between the two groups depends strongly on the initial arrangement of the sensors.

“While this is a rather whimsical application of a sensor network, it demonstrates the unique system behaviors that can arise in truly distributed processing,” says Frampton. Even when nodes follow very simple rules, the behavior of the group can be quite complex. Although this project is not likely to improve knowledge on synchronized calling in nature, it does demonstrate the types of complex behavior patterns that will be important for future developments in sensor networks, Frampton says.