Vviewpoints
DOI: 10.1145/1839676.1839690
Viewpoint
Sensor Networks
for the Sciences
harvard university Ph.D. student Konrad Lorincz installing sensors at Reventador volcano.
MuCh CoMPuTer SCI- eNCe research is inter- disciplinary, bringing together experts from ultiple fields to solve
challenging problems in the sciences,
engineering, and medicine. One area
where the interface between computer
scientists and domain scientists is especially strong is wireless sensor networks, which offer the opportunity to
apply computer science concepts to
obtaining measurements in challenging field settings. Sensor networks have
been applied to studying vibrations on
the Golden Gate Bridge, 1 tracking zebra
movements, 2 and understanding microclimates in redwood canopies. 4
Our own work on sensor networks
for volcano monitoring6 has taught us
some valuable lessons about what’s
needed to make sensor networks successful for scientific campaigns. At the
same time, we find a number of myths
that persist in the sensor network literature, possibly leading to invalid assumptions about what field conditions
are like, and what research problems
fall out of working with domain scientists. We believe these lessons are of
broad interest to “applied computer
scientists” beyond the specific area of
sensor networks.
Our group at Harvard has been col-
laborating with geophysicists at New
Mexico Tech, UNC, and the Instituto
Geofísico in Ecuador for the last five
years on developing wireless sensor
networks for monitoring active and
hazardous volcanoes (see Figure 1). We
have deployed three sensor networks on
two volcanoes in Ecuador: Tungurahua
and Reventador. In each case, wireless
sensors measured seismic and acoustic
signals generated by the volcano, and
digitized signals are collected at a cen-
tral base station located at the volcano
observatory. This application pushes
the boundaries of conventional sensor
network design in terms of the high
data rates involved (100Hz or more per
channel); the need for fine-grained time
synchronization to compare signals col-
lected across different nodes; the need
for reliable, complete signal collection
over the lossy wireless network; and the
need to discern “interesting” signals
from noise.
