Most of the applications of wireless sensor networks involve primarily data collection with in-network processing in which continuous aggregate queries are posed and processed. There are two principle concerns with this type of applications. First, due to the use of batteries, limited power resource has been identified as a major challenge in deploying wireless sensor networks. Second, data is usually expected to be gathered as soon as possible to facilitate the monitoring of and the response to the physical phenomena. In this paper, we tackle these challenges through sensor state scheduling. The proposed technique is based on the observation that there are two types of traffic in sensor networks designed for data aggregation, bottom-up and top-down within an abstract tree structure. We show that it is possible to achieve deterministic schedules for data aggregation with very good performance. Specifically, we develop greedy algorithms to schedule transmission and listening operations for each sensor node to achieve collisionfree communication. We show that the schedules can maximize the time sensor nodes spent on low-power states which helps achieve great energy efficiency, as well as allow fast data aggregation.