Wireless sensor networks (wsns) are almost ev- erywhere, they are exploited for thousands of applications in a densely distributed manner. Such deployment makes wsns one of the highly anticipated key contributors of the big data nowadays. Hence, data aggregation is attracting much attention from researchers as efficient way to reduce the huge volume of data generated in wsns by eliminating the redundancy among sensing data. In this paper, we propose an efficient data aggregation technique for clustering-based periodic wireless sensor networks. Further to a local aggregation at sensor node level, our technique allows cluster-head (CH) to eliminate redun- dant data sets generated by neighboring nodes by applying three data aggregation methods. These proposed methods are based on the sets similarity functions, the one-way Anova model with statistical tests and the distance functions respectively. Based on real sensor data, we have analyzed their performances according to the energy consumption and the data latency and accuracy, and we show how these methods can significantly improve the performance of sensornetworks.