Data Aggregation with Spatially Correlated Grouping Technique on Cluster-based WSNs

Wireless sensor networks have received considerable attention in recent years due to their invaluable potential applications. To achieve long-term deployment, in-network aggregation has been studied and argued as an effective data reduction technique. In this paper, an efficient algorithm to construct and maintain aggregation architecture in cluster-based sensor networks, such as LEACH, is proposed. The architecture takes the spatial and temporal correlations among nodes into account simultaneously to develop the suppression strategies. The main idea is to organize nodes inside the same cluster into highly spatial-correlated groups. One representative node of each group will be selected as base node used as a reference for compressing (using linear regression) the transmissions of the nodes inside the same group. The proposed architecture was evaluated on the real dataset, Intel Lab dataset, and the results indicate that a large amount of transmissions can be reduced without introducing large errors. In contrast to the existing aggregation architectures, such as TAG and TiNA, the results also portray that the hybrid architecture can perform better by considering both spatial and temporal correlations simultaneously.