Computer Science Department
School of Computer Science, Carnegie Mellon University
This dissertation puts forth the claim that more practical solutions can be developed by exploiting several unique deployment- and application-specific properties of typical sensing systems. We show that by slightly relaxing the requirements of exact or fresh answers, we can significantly improve the robustness of a system, without additional resource overheads. We argue that this approach is well suited to sensing systems since optimizing resource usage is one of the important goals of their designs and the applications can often tolerate approximate or slightly stale data. We support the above claim by proposing efficient solutions for robust data collection and storage in a sensing system.
For robust collection of data from wireless sensors, we present Synopsis Diffusion, a novel data aggregation scheme that exploits wireless sensors broadcast communication and sensing applications tolerance for approximate aggregate answers. Synopsis Diffusion, unlike previous schemes, decouples aggregation algorithms from underlying aggregation topologies, enabling highly robust aggregation with energy-efficient multipath routing. We also present Tributary-Delta, a novel adaptive aggregation scheme that e±ciently combines the benefits of existing schemes and uses application-aware adaptation to cope with the dynamics of deployment environments. Under typical loss rates, our techniques can provide five times more accurate results than existing energy-efficient schemes, without additional energy overhead.