Communications - November 2010

of data quality applied to conventional scientific instrumentation.

some myths

First, let us dispel a few common myths about sensor network field deployments.

Myth #1: Nodes are deployed randomly. A common assumption in sensor network papers is that nodes will be randomly distributed over some spatial area (see Figure 2). An often-used idiom is that of dropping sensor nodes from an airplane. (Presumably, this implies that the packaging has been designed to survive the impact and there is a mechanism to orient the radio antennas vertically once they hit the ground.)

Such a haphazard approach to sensor siting would be unheard of in many scientific campaigns. In volcano seismology, sensor locations are typically chosen carefully to ensure good spatial coverage and the ability to reconstruct the seismic field. The resulting topolo-gies are fairly irregular and do not exhibit the spatial uniformity often assumed in papers. Moreover, positions for each node must be carefully recorded using GPS, to facilitate later data analysis. In our case, installing each sensor node took nearly an hour (involving digging holes for the seismometer and antenna mast), not to mention the four-hour hike through the jungle just to reach the deployment site.

Myth #2: Sensor nodes are cheap and tiny. The original vision of sensor networks drew upon the idea of “smart dust” that could be literally blown onto a surface. While such technology is still an active area of research, sensor networks have evolved around off-the-shelf “mote” platforms that are substantially larger, more power hungry, and expensive than their hypothetically aerosol counterparts (“smart rocks” is a more apt metaphor). The notion that sensor nodes are disposable has led to much research that assumes it is possible to deploy many more sensor nodes than are strictly necessary to meet scientific requirements, leveraging redundancy to extend network battery lifetime and tolerate failures.

It should be emphasized that the cost of the attached sensor can outstrip the mote itself. A typical mote costs approximately $100, sometimes with on-board sensors for temperature, light,

Working with domain
scientists has taught
us some valuable
lessons about sensor
network design.

and humidity. The inexpensive sensors used on many mote platforms many not be appropriate for scientific use, confounded by low resolution and the need for calibration. While the microphones used in our volcano sensor network cost pennies, seismometers cost upward of thousands of dollars. In our deployments, we use a combination of relatively inexpensive ($75 or so) geophones with limited sensitivity, and more expensive ($1,000) seismometers. The instruments used by many volcano deployments are in the tens of thousands of dollars, so much that many research groups borrow (rather than buy) them.

Myth #3: The network is dense. Related to the previous myths is the idea that node locations will be spatially homogeneous and dense, with each node having on the order of 10 or more neighbors in radio range. Routing protocols, localization schemes, and failover techniques often leverage such high density through the power of many choices.

This assumption depends on how
closely aligned the spatial resolution of
the desired network matches the radio
range, which can be hundreds of me-
ters with a suitably designed antenna
configuration. In volcanology, the prop-
agation speed of seismic waves (on the
order of kilometers per second) dictates
sensor placements hundreds of meters
apart or more, which is at the practical
limit of the radio range. As a result, our
networks have typically featured nodes
with at most two or three radio neigh-
bors, with limited opportunities for
redundancy in the routing paths. Like-
wise, the code-propagation protocol we
used worked well in a lab setting when
all of the nodes were physically close
to each other; when spread across the
volcano, the protocol fell over, prob-
ably due to the much higher degree of
packet loss.

Lessons Learned

Working with domain scientists has taught us some valuable lessons about sensor network design. Our original intentions were to leverage the collaboration as a means of furthering our own computer science research agenda, assuming that whatever we did would be satisfactory to the geophysicists. In actuality, their data requirements ended up driving our research in several new directions, none of which we anticipated when we started the project.

Lesson #1: It’s all about the data. This may seem obvious, but it’s interesting how often the actual data produced by a sensor network is overlooked when designing a clever new protocol or programming abstraction. To first approximation, scientists simply want all of the data produced by all of the sensors, all of the time.