||In-network query processing for sensor networks is a cross-layer paradigm, in which database-style queries are injected into networked sensor nodes and query results over online sensory data are produced by these nodes. In this thesis work, we take a scheduling approach to improve the performance and to provide quality guarantees for in-network sensor query processing. Specifically, we propose the following three scheduling schemes: (1) DCS (Distributed, Cross-layer Scheduling), (2) AHS (Adaptive, Holistic Scheduling), and (3) QAS (Quality-Aware Scheduling). Each of the three schemes addresses a distinct aspect of the problem; collectively, they serve a wide arrange of applications. In DCS, each node utilizes cross-layer information to determine time slots for query processing and arranges its own sleep and communication timings in coordination with its neighbors for reliable query result reporting. AHS further adapts the schedules of both query operators and wireless communication to runtime dynamics for continual efficiency. QAS maximizes the total quality profit of a multi-query workload through prioritizing individual queries and modeling quality status. Our results on both real-world and simulated sensor networks demonstrate the efficiency of the proposed schemes.