An Analysis of Variance-Based Methods for Data Aggregation in Periodic Sensor Networks

Given the vast area to be covered and the random deployment of the sensors, wireless sensor networks (WSNs) require scalable architecture and management strategies. In addition, sensors are usually powered by small batteries which are not always practical to recharge or replace. Hence, designing an efficient architecture and data management strategy for the sensor network are important to extend its lifetime. In this paper, we propose energy efficient two-level data aggregation technique based on clustering architecture with which data is sent periodically from nodes to their appropriate Cluster-Heads (CHs). The first level of data aggregation is applied at the node itself to eliminate redundancy from the collected raw data while the CH searches, at the second level, nodes that generate redundant data sets based on the variance study with three different Anova tests. Our proposed approach is validated via experiments on real sensor data and comparison with other existing data aggregation techniques.