K-means based clustering approach for data aggregation in periodic sensor networks

In-network data aggregation becomes an important technique to achieve efficient data transmission in wireless sensor networks (WSN). Energy efficiency, data latency and data accuracy are the major key elements evaluating the performance of an in-network data aggregation technique. The trade-offs among them largely depends on the specific application. For instance, prefix frequency filtering (PFF) is a good recently example for an in-network data aggregation technique that optimizing energy consumption and data accuracy. The objective of PFF is to find similar data sets generated by neighboring nodes in order to reduce redundancy of the data over the network and thus to preserve the nodes energy. Unfortunately, this technique has a heavy computational load. In this paper, we propose an enhanced new version of the PFF technique called KPFF technique. In this new technique, we propose to integrate a K-means clustering algorithm on data before applying the PFF on the generated clusters. By this way we minimize the number of comparisons to find similar data sets and thus we decrease the data latency. Experiments on real sensors data show that our new technique can significantly reduce the computational time without affecting the data aggregation performance of the PFF technique.