A Two Tiers Data Aggregation Scheme for Periodic Sensor Networks

The expected lifetime of any wireless sensor network is a critical issue since sensor nodes are powered by small batteries. The propagation of redundant highly correlated data is costly in terms of system performance, and results in energy depletion, network overloading, and congestion. Data aggregation is regarded as an effective technique to reduce energy consumption and prevent congestion. This paper objective is to identify near duplicate nodes that generate similar sets of collected data in periodic applications. We propose a new prefix filtering approach that avoids computing similarity values for all possible pairs of sets. We define a new filtering technique based on the quality of information. To the best of our knowledge, the proposed algorithm is a pioneer in using "sets similarity functions" for data aggregation in sensor networks. To evaluate the performance of the proposed method, experiments on real and synthetic sensor data have been conducted. The analysis and the results show the effectiveness of our method dedicated to sensor networks.