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Combining compression and clustering techniques to handle big data collected in sensor networks

The data collected by sensors are of a huge amount. Hence, wireless sensor networks (WSNs) have been considered as one of the big data domains. Therefore, current research has been focused on data compression and data clustering as efficient techniques to reduce big data collection in WSNs thus enhancing their lifetime. This paper proposes a two-level data reduction approach for periodic sensor-based big data framework. The sensors constitute the first level and they use a data compression model based on the Pearson coefficient in order to reduce the amount of data collected periodically in each sensor. The second level is applied at intermediate nodes, called aggregators. The aggregator has an objective to eliminate data redundancy collected by neighboring nodes using an adapted version of Kmeans clustering method. Simulation on real data sensors shows the effectiveness of our technique in reducing the big data collected in WSNs and enhancing network lifetime, compared to other existing techniques.