Abstract: Data aggregation in Wireless sensor networks (WSNs) has attracted wide attention since it could reduce the wireless communication data amount and thus save power consumption, which is one of the top concerns in low-power WSN systems. Given a Data Aggregation Tree (DAT) for data query purpose in a WSN and the expected query accuracy, we will show the feasibility to adaptively adjust the waiting period in each DAT level so that the end-to-end query delay could be optimized, i.e., without unnecessary pause in an aggregation node. This study will introduce some important data aggregation parameters (such as desired query accuracy and practical query resolution, etc.) to study the optimal, low-cost timing control scheme. For such an adaptive timing control scheme, we propose to use a Finite State Machine (FSM)-based auto-feedback control algorithm to adjust the waiting period of each DAT node to timely respond to the varying query results performance. For instance, in the beginning of the query task, the FSM algorithm should be able to use the fast speed to increase the waiting period so that more query results can be collected, while the algorithm should use micro-adjustments to slowly adjust the waiting period after the query accuracy has reached a certain level. From quantitative analysis, networking simulations and experimental setup perspectives, we will investigate the efficiency of our FSM-based timing control algorithm in terms of achieving a good trade-off between query accuracy and end-to-end query response delay. Our sensor network simulation results and hardware experiments have verified the validity of our proposed timing control scheme. In addition, we will also compare different FSM algorithms in different WSN data aggregation scenarios.