The efficiency of distributed sensor networks depends on an optimal trade-off between the usage of resources and data quality. This workshop paper addresses the problem of optimizing this trade-off in self-configured distributed sensor networks. In our case-study example, we investigate a quadtree network topology and describe how we integrate a fully distributed node controller and field estimation algorithm. In a further step, we present a variant control algorithm, which continuously adapts network sampling and node activity to match spatio-temporal field variability. Realistic simulations are performed on the e-puck robot platform, and show that the proposed sampling strategy potentially economizes 20% of resource usage.