Wireless sensor networks usually have limited energy and transmission capacity, and they can’t match the transmission of a large number of data. So, it is necessary to perform in-network compression or aggregation of the raw data sampled by sensors. By designing a ring topology, this paper proposes an algorithm for wavelet based spatio-temporal data compression in wireless sensor networks. The algorithm is capable of supporting a broad scope of wavelets that can simultaneously explore the spatial and temporal correlations among the sensory data. In this algorithm, the data in sensor networks are abstracted as a matrix, and the temporal and spatial correlation is then captured by the column and row wavelet transform respectively. The performance of the algorithm is qualitatively analyzed from the viewpoints of energy and delay. Theoretically and experimentally, it is concluded that the proposed algorithm can effectively explore the spatial and temporal correlation in the sensory data and provide a significant reduction in energy consumption and delay.