Divide and Conquer
As if ready to take off themselves, 50-odd butterfly-sized motes cling to the ceiling and walls of Deborah Estrin’s lab at UCLA, monitoring temperature, light, and motion. Others lie dismantled on desktops and benches. A few of the motes even have wheels; they roll across the floor under their own propulsion, practicing for a day when they’ll move around to find the best radio reception or deliver a battery recharge to a failing neighbor. “Here’s a picture of the connectivity,” says Estrin, holding up a sheet of paper with an incomprehensible tangle of lines on it. It looks like a plate of spaghetti: the number of communication pathways explodes as more nodes are added, making the network more and more crash-prone.
The solution being tested in Estrin’s lab: divide and conquer. Think of it as organizing a big dinner party, she says. Meaningful conversations can’t occur unless people take turns speaking and listening. And high-level communication is most efficient if people organize themselves into clusters and elect an individual to speak for each cluster. Therefore the nodes cluster themselves and adjust on the fly, changing clusters opportunistically to optimize both power consumption and the flow of information through the network.
The next challenge is simply how to channel the flood of data. The idea is to put processing into each node, allowing it to condense raw data into patterns and pass along fewer bits than it received. The motes above Estrin’s head, for example, could follow her movements and alert their neighbors, which figure out the direction she’s walking and transmit just that information-not the entire record of her movements-to a database on a mother node. This node can recommend that lights be turned off, for example, if it decides that Estrin has left the room and no other people are present. Processing data a little at a time throughout the network, says Estrin, is a first step toward programming the system to help make intelligent decisions. It also saves precious battery power.
To be truly useful, a sensor network should send users only its analyses of interesting events, not the raw bits themselves. “People want answers, not numbers,” points out Steven Glaser, a professor of civil and environmental engineering at UC Berkeley whose group uses sensor nets to study seismic activity.
Among the answers that engineers and seismologists like Glaser want: how do earthquakes affect individual components of buildings, and how do structures respond to localized variations in an earthquake’s strength? A UCLA team led by Paul Davis, a geophysicist and principal investigator at Estrin’s center, is deploying a 50-node array of seismic sensors across the campus in an attempt to learn part of the answer. The first step is just to accumulate the data, recorded from the ground at 100-meter intervals-a much higher resolution than that provided by current seismic sensors, which are spaced kilometers apart, says Davis. The researchers will then compare how the ground shakes to vibrations measured at the same time inside a campus building wired by the U.S. Geological Survey after the Northridge, CA, quake of 1994.
Researchers at UCLA are deploying a 50-node sensor network to monitor seismic activity on a finer scale than ever before. Superimposed on this map of the UCLA campus are the locations of the ground vibration sensors (stars), spaced 100 meters apart.
The goal is to develop a model of how fine-scale seismic activity affects different structures. Such a model-programmed into portable sensor nets that could be deployed temporarily in city neighborhoods-could help urban planners learn where geological conditions tend to magnify quakes and how to make buildings in those areas safer. In the future, sensors placed near fault lines could even detect approaching seismic waves and trigger alarms, giving building occupants precious seconds to get to safer areas. But, Davis says, “That’s blue-sky stuff.”
Google for the Physical World
Smart, autonomous, and self-aware: that’s the ultimate vision for sensor nets. In many ways, it is blue-sky. But two industry projects provide glimpses of a networked future.
There is a danger that accessing the data collected by sensor networks will be like “drinking from a fire hose, only worse,” says Feng Zhao, manager of the Embedded Collaborative Computing research area at the Palo Alto Research Center in California. In other words, being inundated with too much data can be just as paralyzing as not having enough. It’s a dilemma that anyone using the Web is well aware of. And, says Zhao, the solution for sensor networks may be similar. In an effort to construct user-friendly interfaces for sensor networks, Zhao’s group is experimenting with a new breed of search engine that he describes as “like Google for the physical world.”
Imagine, Zhao explains, logging onto the Internet and typing in, “Does my lawn need more water?” The network would translate the question into a standardized database query, examine figures from moisture sensors around your home, and send back a prompt yes or no. Similar systems for supply chain management and security could be available in five to seven years, says Zhao. At warehouses, managers could quiz shelf-mounted sensors about inventory trends, while guards in secure facilities could program smart networks of motion sensors to sound alarms when they notice suspicious patterns of movement.
Eventually, sensor nets may even seem alive. At a U.S. Army base in Fort Leonard Wood, MO, this April, Sensoria engineers demonstrated a disturbingly self-aware system that physically re-arranges itself in response to changing conditions. As 80 spectators watched, an M1-A1 Abrams battle tank rumbled across a field with a plow attached to its front, blazing a trail through a thicket of unarmed, 12-centimeter-diameter mines. After the tank crushed a half-dozen or so of the mines and proceeded on its way, the remaining mines redistributed themselves to fill the gap behind the tank-hopping through the air with firecracker pops emanating from tiny rocket boosters.
The mines accomplished this feat by emitting and listening for acoustic pulses that helped them locate their neighbors to within a few centimeters, says Kaiser. A disturbance in the network prompts the mines to figure out which neighbors have been moved or destroyed and calculate how to redistribute themselves. On a real battlefield, such smart mines could defeat enemy mine-clearing efforts, or even move out of the way for friendly forces and then reestablish defenses behind them.
Despite such dramatic demonstrations of the power of wireless sensor nets, it’s hard to predict whether defense, manufacturing, or some as-yet-unknown field will play host to their killer app. “It’s like PCs in the early 1980s. People thought they would be used mainly to balance checkbooks,” says Delin. As for the near-term commercial market, it will be a “delectably messy environment for a while,” with plenty of opportunity for newcomers, predicts Ember’s Poor. That’s because the potential applications are all around us-anywhere useful information can be extracted from our environment. When today’s research is translated into inexpensive, crashproof products, it may signify nothing short of a merger between the virtual world and the physical world. “It’s going to happen,” says Zhao. “The question is, how soon?”