Meeting Ecologists Requirements with Adaptive Data Acquisition

Ecologists instrument ecosystems with in-situ sensing to collect mea-
surements. Sensor networks promise to improve on existing data acqui-
sition systems by interconnecting stand-alone measurement systems into
virtual instruments. Such ecological sensor networks, however, will only
fulll their potential if they meet the scientists requirements. In an ideal
world, an ecologist expresses requirements in terms of a target dataset,
which the sensor network then actually collects and stores. In fact, failures
occur and interesting events happen making uniform, systematic ecosys-
tem sampling neither possible nor desirable. Today, these anomalous sit-
uations are handled as exceptions treated by technicians that receive an
alert at deployment time. In this paper, we detail how ecological sensor
networks can adapt to anomalies and maximize the utility of the col-
lected datasets. More specically, we present the design of a controller
that continuously maintains its state based on the data obtained from the
sensor network (as well as external systems), and congures motes with
parameters that satisfy a constraint optimization problem derived from
the current state. We describe our implementation, discuss its scalability,
and discuss its performance in the context of a case study.