Energy Efficient Filtering Techniques for Data Aggregation in Sensor Networks

Minimizing latency is a major issue for data aggregation in wireless sensor networks (WSNs). Hence, the proposed algorithms must achieve the minimum delay in data delivery while decreasing the energy consumption. In this paper, we propose a new version of the prefix frequency filtering technique (PFF) proposed by [1], which aims to minimize aggregation latency. PFF finds similar sets of data generated by nodes in order to reduce redundancy in data over the network, thus, nodes consume less energy. While in the enhanced version of the PFF technique, called PPSFF, we propose a positional filtering that exploits the order of readings both in the prefix and the suffix of a set and leads to upper bound estimations of similarity scores. Experiments on real sensor data show that our enhancement can significantly improve the latency of the PFF technique without affecting its performance.