Data aggregation for periodic sensor networks using sets similarity functions

Energy is a major constraint in wireless sensor networks. Data Aggregation constitutes a fundamental mechanism for energy optimization. The idea is to minimize redundancy from the raw data captured by the sensors, minimizing the number of transmissions to the sink and thus saving energy. Since the data is often captured on a periodic basis, and sensor nodes detect common phenomena, a periodic based protocol that manages collected data sets can help to preserve the scarce energy. This paper proposes a new filtering technique for identifying duplicate sets of periodically captured data. We suggest a data aggregation model based on set joins similarity functions that conserves data integration while eliminating inherited redundancy. We show through the result that our approach offers significant data reduction by eliminating in-network redundancy and sending only necessary information to the sink.