This dissertation presents architectural solutions aiding the design and deployment of scientific sensor networks. Wireless sensor networks composed of tens or hundreds of low-power, resource-constrained devices, can aid scientific exploration by providing high-fidelity data at scales difficult to achieve with traditional instrumentation. Doing so requires sensor networks provide high-fidelity data for scientific study; identify the data most valuable to the application and devote system resources to capturing it; and respond to ongoing fluctuations in distributed energy availability. Our thesis is that by managing distributed resource usage, we can improve the data quality provided by sensor networks to scientific applications.

We make three core contributions. First, working with volcano seismologists, we have designed and evaluated the first sensor network enabling the study of erupting volcanos. Through three field studies we validate that it can produce data suitable for scientific analysis. We found verifying the timing accuracy of our data both difficult and important and were able to develop a new post-hoc time-rectification technique that can recover accurate timestamps even in the face of timing protocol failures.

Second, we developed Lance, a general-purpose architecture targetting high data-rate sensor networks. Lance manages data-collection in order to direct energy and bandwidth resources toward the most interesting signals. Valuable data is identified using a two-tiered architecture leveraging the increased visibility of a network controller while limiting the overhead of centralized control. Lance has been used to support the volcano-monitoring application as well as the Mercury project, which enables monitoring of patients with neuromuscular disorders. We show that Lance's resource allocation heuristic achieves near-optimal efficiency, within 5% of an offline optimal solution in most cases.

Finally, we present IDEA (Integrated Distributed Energy Awareness), a sensor network service allowing application components to tune their behavior in response to changing energy availability. This allows the network to adjust energy load as availability changes, particularly important when energy-harvesting capabilities are being used. We have used IDEA to build energy-aware routing and MAC protocols and measured the performance improvement they provide over non-aware solutions. By better distributing energy consumption, IDEA extends the network's lifetime by up to 27%.