In this paper, we develop a novel framework that attempts to reduce network traffic for error-bounded data collection in wireless sensor networks. In many sensor applications, it is acceptable that the monitoring results evaluated based on collected data might deviate from the exact results; as long as the error is bounded by a certain threshold. One well-known technique for error-bounded data collection is data filtering, which explores temporal data correlation to suppress data updates. A concrete scheme was proposed in . The data collection is divided in rounds. A filter is installed on each node and the total filter size is constrained by the user-specified error budget. Intuitively, if the data change from the last update report is smaller than the filter size, the current update is suppressed, i.e., not to report to the base station. To adapt to system dynamics, the sizes of all filters are periodically shrunk and the left-over budget is re-allocated to the node with the highest load. Many follow up studies can be found .