Wireless sensor networks (WSN) are composed many of sensor nodes which are a infrastructure-less wireless networks to monitor physical or environmental conditions in order to cooperatively forward their sensing data through the sensor network to a main location or sink. These wireless sensor nodes are very small in size, have limited processing capability and very low battery power. This restriction of low battery power makes the sensor network prone to failure. Thus, data aggregation is one of very crucial technique in wireless sensor networks to avoid down fail of the network. As a trend, in recent years, researchers have been adopting the spatio-temporal correlation in the wireless sensor environment. This spatio-temporal correlation is utilized strong characteristics, such as extract spatial features, data is collected across both in space and time, describes a phenomenon in a certain location and time that facilitate the visualization of sensor data distributed on the wireless sensor environment. In this paper, by adopting Spatio-temporal correlation strong characteristics, we propose an energy efficient data aggregation scheme using Spatio-temporal correlation which applies on wireless sensor network to improve the energy efficiency. The feasible aggregation scheme is given for obtaining sensor information of nodes on the environment based on the local density of the node relative to the neighbor node. The hierarchical distribution of nodes makes the sensor information to be aggregated by parent nodes to reduce the energy consumption on the wireless sensor network. From the experimental results, the performance investigation of the system is executed using Network Simulator two (NS2) simulation tool. Finally, we can find our aggregation scheme still has a good performance as compared with the original LEACH and HEED algorithms. The simulation result displays that our system to improves the node lifetime, the energy consumption, throughput and aggregate delay. Therefore, the performance metrics shows that our scheme is feasible and effective. Keywords: WSNs, Spatio-Temporal Correlation, Cluster Head Node, Data Aggregation, Energy Consumption.