Spatio-Temporal Hierarchical Data Aggregation Using Compressive Sensing (ST-HDACS)

The problem of power-efficient data aggregation in wireless sensor networks (WSNs) using Compressive Sensing (CS) to reduce the amount of data communicated is addressed here. Existing CS-based data aggregation methods can be categorized as either those that apply CS spatially to minimize the amount of data to be communicated in the routing path, or those that seek to minimize the amount of data by applying CS temporally at each sensor. A recently reported scheme that is described as a Spatial-Temporal CS scheme randomly selects a subset of data but does not apply compression in the routing path. Here we formulate a spatial-temporal data collection model in WSNs and refer to it as Spatial-Temporal Hierarchical Data Aggregation using Compressive Sensing (ST-HDACS). The idea underlying ST-HDACS consists of two key components: Firstly, for each time snapshot of data collected in the network, a subset of nodes is randomly selected and designated for data sensing and transmission. A power-efficient Adaptive Hierarchical Data Aggregation (A-HDACS) scheme is incorporated in our work to compress the spatial data to be communicated in the routing path. Secondly, after performing data collection over a designated time period, a Matrix Completion (MC) problem is executed in the fusion center to recover the data for the entire network over the full data collection period. The performance of the proposed method is evaluated and it is demonstrated that ST-HDACS scheme reduces the amount of data for transmission and improves the associated energy consumption more effectively than existing CS-based data aggregation schemes.