Compressive sensing (CS) is a burgeoning technique being applied to diverse areas including wireless sensor networks (WSNs). In WSNs, it has been applied in the context of data gathering and aggregation, particularly aimed at reducing data transmission cost and improving power efficiency. Existing CSbased data gathering work in WSNs utilize the property that under certain conditions, only O(K log N ) CS random measurements can represent a K-sparse signal of length N . In previous work fixed and identical compression thresholds were assumed for the entire network resulting in less efficient solutions. In this paper, we present a novel data aggregation architecture model that integrates a multi-resolution hierarchical structure with CS to further optimize the amount of data transmitted. Our key idea is to set up multiple compression thresholds adaptively based on the cluster sizes at different levels. The advantages of the proposed aggregation model in contrast to other state-of-the-art related work are measured in terms of total amount of data for transmission, data compression ratio and energy consumption. We implement the proposed data aggregation scheme on a SIDnet-SWANS platform, a discrete event simulator commonly used for WSN simulations. Our simulation results demonstrate that the proposed CS-based hierarchical data aggregation model guarantees accurate signal recovery performance; meanwhile, it also obtains substantial energy savings compared to other existing methods.
Thus, an energy-efficient technique: Hierarchical Data Aggregation using Compressive Sensing (HDACS) REF was presented based on a multi-resolution hierarchical clustering architecture and HCS.
Power-efficient hierarchical data aggregation using compressive sensing in WSNs
2013 IEEE International Conference on Communications (ICC)
2013 IEEE International Conference on Communications (ICC)
Computer Science