Compressive Multi-attribute Data Gathering using Hankel Matrix in Wireless Sensor Networks

For heterogeneous WSNs with various types of sensors, compressive data gathering method requires more measurements due to the increased multiple attributes. In this letter, a compressive multi-attribute data gathering method using low-rank Hankel matrix is proposed to reduce the required measurements and improve the recovery accuracy in heterogeneous WSNs. Beyond utilizing just the spatiotemporal correlation of the raw sensed data with compressed sensing, the proposed method further enforces the low-rank block Hankel matrix to exploit the inherent correlation among multi-attribute data. Experimental results demonstrate that the proposed method can significantly improve the recovery accuracy of multi-attribute data compared with the existing solutions in WSNs.